A survey of visual analytics techniques for machine learning
该研究由六位学者共同完成的研究资料（链接资料）组成：Yuan Jun、Chen Chang Jian、Yang Weik ai、Liu Meng chen、Xia Jiaz hi以及Liu Shix ia（注释部分：每个链接后面跟着一个带有中文数字的注释编号，并且在括号内交换了姓名的姓氏和名字）
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Abstract
Visual analytics for machine learning has seen significant advancements in the field of visualization. With the aim to identify promising research areas and gain practical insights into applying relevant techniques, we conducted a comprehensive review of 259 papers published over the past decade alongside key studies prior to 2010. This effort resulted in the development of a taxonomy structure, which is organized into three primary categories: methods prior to model development, approaches during model construction, and strategies post-model implementation. Each category is further detailed through distinctive analytical tasks, with specific examples drawn from notable recent studies. Additionally, we explored emerging challenges and highlighted promising directions for future research that could benefit visual analytics scholars.
Keywords
可视化分析工具结合机器学习算法对数据质量进行评估，并采用特征选择方法优化模型的解释能力
1 Introduction
The recent success of artificial intelligence applications relies on advancing capabilities within machine learning systems [1]. Over a span of ten years alone, numerous visual analytics methodologies have emerged aimed at enhancing machine learning's explainability by making it more transparent while also ensuring reliability through robust trustworthiness measures. By integrating interactive visualization tools with cutting-edge machine learning techniques, these efforts seek to comprehensively dissect key elements within a system's operational lifecycle—ultimately striving to elevate overall functionality. As an illustrative example demonstrating this trend further still: research into enhancing interpretability within deep convolutional neural networks has significantly improved our understanding not just of these systems but also their inner workings—thereby garnering sustained attention from both academic and industry communities [1-4].
The increasing popularity of visual analytics techniques in machine learning has led to an urgent need for a systematic overview of this domain, which can aid in comprehending how visualization techniques are conceptualized and implemented within machine learning pipelines. The field has seen various early attempts to summarize advancements from diverse perspectives. For instance, Liu et al. [5] focused on summarizing visualization methods for text analysis. Lu et al. [6] provided a survey on visual analytics techniques for predictive modeling. Recently, Liu et al. [1] presented a detailed analysis of machine learning models from a visual analytics viewpoint. Sacha et al. [7] examined multiple example systems and established an ontology for visual analytics-supported machine learning. However, existing surveys either concentrate on specific areas within machine learning (e.g., text mining [5], predictive modeling [6], model interpretation [1]) or aim to construct an ontology based solely on example techniques [7].
In this paper, we propose a systematic approach to explore visual analytics techniques in machine learning, with a focus on the entire machine learning pipeline. By examining works from the visualization community, we identify significant contributions from the AI field as well. Notably, several researchers have developed methods to enhance the interpretability of machine learning models. For instance, Selvaraju et al. [8] introduced techniques for identifying image regions that are most influential in classification outcomes. Readers interested in detailed surveys can refer to Zhang and Zhu [9] and Hohman et al. [3]. Over the past decade, we have compiled a comprehensive collection of 259 high-quality papers from leading venues. Based on the stages of machine learning—pre-construction, during construction, and post-construction—we categorize these studies into three main themes: data quality improvement before model development, model understanding and optimization during construction, and post-construction data analysis. Each theme is illustrated with representative examples that highlight key methodologies. This review identifies six critical research directions and unresolved challenges in the field of visual analytics for machine learning. We hope this work will stimulate further discussion among researchers and practitioners seeking to advance visual analytics tools for machine learning applications.
2 Survey landscape
2.1 Paper selection
In this paper, we centered our investigation on visual analytics techniques that contribute to the creation of explainable, trustworthy, and reliable machine learning applications. To conduct a detailed examination of visual analytics techniques for machine learning, we systematically reviewed top-tier venues from 2010 to 2020: InfoVis, VAST, Vis (later SciVis), EuroVis, PacificVis, IEEE TVCG, CGF, and CG&A. This systematic review was carried out by three Ph.D. candidates with over two years of experience in visual analytics research. Our methodology followed that outlined in a text visualization survey [5]. Specifically, we first examined the titles of papers from these venues to identify candidate works. Next, we reviewed the abstracts of these candidate papers to further assess whether they focused on visual analytics techniques for machine learning. If the title and abstract did not offer clear insights, we then read the full texts to make our final determination. Additionally to our systematic review of these venues, we also identified representative earlier works or those from other venues such as Profiler [10].
Following this process, 259 papers have been selected. A comprehensive table is provided in Table 1, which displays detailed statistics. Over the past ten years, there has been a significant rise in machine learning techniques; consequently, this field has garnered substantial research attention.
Table 1Categories of visual analytics techniques for machine learning and representative works in each category (number of papers given in brackets)
| Technique category | Papers | Trend | |
|---|---|---|---|
| Before model building | Improving data quality (31) | [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25],[26], [27], [10], [28], [29], [30], [31], [32], [33], [34], [35],[36], [37], [38], [39], [40], [41], [42], [43] |
|
|Improving feature quality (6)|[44], [45], [46], [47], [48], [49]|
||
|During model building|Model understanding (30)|[50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79]|
|
|Model diagnosis (19)|[80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98]|
||
|Model steering (29)|[99], [100], [101], [102], [13], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126]|
||
|After model building|Understanding static data analysis results (43)|[127], [128], [129], [130], [131], [132], [133], [134], [135], [136], [137], [138], [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162],[163], [164], [165], [166], [167], [168], [169]|
|
|Understanding dynamic data analysis results (101)|[170], [171], [172], [173], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185], [186], [187], [188], [189], [190], [191], [192], [193], [194], [195], [196], [197], [198], [199], [200], [201], [202], [203], [204], [205], [206], [207], [208], [209], [210], [211], [212], [213], [214], [215], [216], [217], [218], [219], [220], [221], [222], [223], [224], [225], [226], [227], [228], [229], [230], [231], [232], [233], [234], [235], [236], [237], [238], [239], [240], [241], [242], [243], [244], [245], [246], [247], [248], [249], [250], [251], [252], [253], [254], [255], [256], [257], [258], [259], [260], [261], [262], [263], [264], [265], [266], [267], [268], [269], [270]|
||
Show table
2.2 Taxonomy
In this section, we conduct comprehensive analysis of the collected visual analytics works to systematically identify key research trends. These works are classified using a typical machine learning pipeline [11], which is designed to address real-world challenges. As illustrated in Figure 1, such pipelines generally consist of three main phases: (1) data preprocessing prior to model development; (2) construction of machine learning models; and (3) implementation of models post-construction. Consequently, various visual analytics techniques applicable to machine learning can be appropriately categorized within these three phases: those applied during preprocessing steps; those utilized during the model-building phase; and those implemented post-construction.
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Fig. 1An overview of visual analytics research for machine learning.
2.2.1 Techniques before model building
The central objective of visual analytics techniques prior to model building is to assist model developers in preparing the data for effective modeling. The quality of the data is primarily influenced by its inherent characteristics and the features selected. Consequently, there are two primary research streams: visual analytics for enhancing data quality and feature engineering.
Data integrity can be enhanced through multiple strategies, including addressing incomplete data attributes and rectifying inaccurate data labels. Historically, these tasks were primarily carried out manually or through automated means, such as learning-from-crowds algorithms [12], which seek to infer true labels from noisy crowd-sourced annotations. To minimize experts' involvement or enhance the outcomes of automated approaches, several studies have incorporated visual analytics techniques to iteratively enhance data quality. The table below illustrates significant advancements in this domain over the past decade.
The role of feature engineering lies in selecting the most effective features for model training. For instance, within computer vision applications, alternative approaches such as utilizing HOG (Histogram of Oriented Gradient) features can be employed instead of relying on raw pixel data. Furthermore, visual analytics often involves an interactive process where users can engage with feature selection through intuitive and iterative methods. In the context of deep learning advancements during recent years,fature extraction and feature design have become predominantly carried out through neural network models. By aligning with these emerging trends, there has been a noticeable decline in research focus on this area over the past few years (see Table 1 for reference).
2.2.2 Techniques during model building
Model construction serves as a pivotal phase in the development of successful machine learning applications. Advancing visual analytics techniques to support model construction has become an increasingly prominent research direction within the field of visualization (see Table 1). The survey classifies current methods based on their analytical objectives: model understanding, diagnosis, or steering. Model understanding methods primarily focus on providing visual insights into how models operate, such as illustrating how parameter adjustments influence the model's functionality and elucidating why a specific input yields a particular output from the model. Model diagnosis methods aim to identify issues in model training through interactive exploration of the training process. Model steering methods are designed to enhance model performance through interactive means. For instance, Utopian [13] enables users to interactively merge or split topics while automatically adjusting other topics accordingly.
2.2.3 Techniques after model building
Once a machine learning model has been developed and put into operation, it becomes essential to assist users from various professional backgrounds in comprehending its outputs in an accessible manner. This comprehension is key to building trust in the system's outputs. To achieve this objective, diverse visual analytics techniques have been devised for exploring and interpreting complex outputs across numerous domains. These techniques differ significantly from those employed during the development phase when aiming to understand models; instead of focusing on explaining how models operate internally, they prioritize presenting outputs in intuitive ways that facilitate exploration. Given that many such approaches are either driven by data or specific applications, in this survey we group them based on their analysis focus.
3 Techniques before model building
In constructing a model, two essential tasks must be completed: data preprocessing and feature engineering. These tasks are crucial (as cited in references [271], [272]) since poor-quality data and features tend to negatively impact machine learning model performance. Data quality challenges often manifest in missing values within instances or their labels as well as outliers or noise contamination. Feature quality challenges typically involve irrelevant or redundant features that can hinder model performance. Addressing these challenges manually is time-consuming; however, automated methods may not be equally efficient. To mitigate this issue while maintaining model accuracy, advancements in visual analytics techniques have been developed to alleviate experts' burden while enhancing the effectiveness of automated approaches for generating high-quality data and features (as referenced in [303]).
3.1 Improving data quality
The dataset comprises instances along with their corresponding labels [273]. From this viewpoint, existing initiatives aimed at enhancing the quality of data either focus on improving instance-level characteristics and addressing label-related issues.
3.1.1 Instance-level improvement
注
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Fig. 2OODAnalyzer, a method designed to identify out-of-distribution samples and offer contextual explanations for them. This work is permitted by Ref. [21] under the copyright of © IEEE in the year 2020.
When analyzing time-series data, several challenges emerge because the temporal nature of time induces specific quality issues that require analysis in a temporal context. To address these challenges, Arbesser et al. [15] introduced Visplause, a visual analytics system designed to assess the quality of time-series data. The system presents anomaly detection results, such as anomaly frequencies and their distribution over time, in a tabular format. To tackle scalability concerns, data are organized hierarchically based on metadata, enabling concurrent analysis of anomaly groups (e.g., abnormal time series sharing common characteristics). Additionally, KYE [23] enhances anomaly detection by identifying anomalies missed by automatic methods. Time-series data are displayed using heatmaps, where abnormal patterns (e.g., regions with unusually high values) highlight potential anomalies. Click stream data represent another significant category within time-series analysis. Segmentifier [22] was developed to improve the exploration and refinement of click stream data through an iterative segmentation process. Users can examine segments across three coordinated views at varying levels of detail and refine them using filtering, partitioning, and transformation techniques. Each refinement generates new segments for further analysis.
Addressing uncertainties in enhancing data quality, Bernard et al. [17] created a visual analytics platform designed to depict changes in data alongside uncertainties stemming from various preprocessing techniques. This platform empowers experts with an understanding of these methods' impacts and guidance in selecting appropriate approaches, allowing them to minimize task-irrelevant aspects while retaining crucial data components.
As data pose the risk of exposing sensitive information, several recent studies have concentrated on maintaining data privacy during the process of enhancing data quality. For tabular datasets, Wang et al. [41] introduced a Privacy Exposure Risk Tree to depict exposure risks inherent to such datasets and a Utility Preservation Degree Matrix to illustrate variations in utility under privacy-preserving operations. To safeguard privacy in network datasets, Wang et al. [40] developed an analytical system named GraphProtector. Node priorities are initially determined based on their significance, with important nodes assigned low priority settings to minimize potential modifications. Utilizing node priorities and utility metrics, users can evaluate and compare various privacy-preserving operations and select one that aligns with their expertise and knowledge.
3.1.2 Label-level improvement
According to whether the data exhibit noisy or corrupted labels, existing studies can be categorized into two main approaches: one focused on enhancing the reliability of noisy label data, and another that supports interactive labeling processes.
Crowdsourcing provides a cost-effective way to collect labels. However, annotations provided by crowd workers are usually noisy [271,276]. Many methods have been proposed to remove noise in labels. Willett et al. [42] developed a crowd-assisted clustering method to remove redundantexplanations provided by crowd workers. Explanations are clustered into groups, and the most representativeones are preserved. Park et al. [35] proposed C 22A that visualizes crowdsourced annotations and worker behavior to help doctors identify malignant tumors in clinical videos. Using C 22A, doctors can discard most tumor-free video segments and focus on the ones that most likely to contain tumors. To analyze the accuracy of crowdsourcing workers, Park et al. [34] developed CMed that visualizes clinical image annotations by crowdsourcing, and workers’ behavior. By clustering workers according to their annotation accuracy and analyzing their logged events, experts are able to find good workers and observe the effects of workers’ behavior patterns. LabelInspect [31] was proposed to improve crowdsourced labels by validating uncertain instance labels and unreliable workers. Three coordinated visualizations, a con-fusion (see Fig. 3(a)), an instance (see Fig. 3(b)), and a worker visualization (see Fig. 3(c)), were developed to facilitate the identification and validation of uncertain instance labels and unreliable workers. Based on expert validation, further instances and workers are recommended for validation by an iterative and progressive verification procedure.
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Fig. 3 LabelInspector, a user-friendly interactive tool designed to validate uncertain instance labels and identify unreliable workers, was adapted from Ref. [31]. Permission to reproduce this figure is granted by © IEEE 2019.
Although the aforementioned methods can effectively improve crowdsourced labels, crowd information is not available in many real-world datasets. For example, the ImageNet dataset [277] only contains the labels cleaned by automatic noise removal methods. To tackle these datasets, Xiang et al. [43] developed DataDebugger to interactively improve data quality by utilizing user-selected trusted items. Hierarchical visualization combined with an incremental projection method and an outlier biased sampling method facilitates the exploration and identification of trusted items. Based on these identified trusted items, a data correction algorithm propagates labels from trusted items to the whole dataset. Paiva et al. [33] assumed that instances misclassified by a trained classifier were likely to be mislabeled instances. Based on this assumption, they employed a Neighbor Joining Tree enhanced by multidimensional projections to help users explore misclassified instances and correct mislabeled ones. After correction, the classifier is refined using the corrected labels, and a new round of correction starts. Bäuerle et al. [16] developed three classifier-guided measures to detect data errors. Data errors are then presented in a matrix and a scatter plot, allowing experts to reason about and resolve errors.
大多数上述方法都从带噪声的标记数据集开始进行处理；然而，并非所有数据集都包含这样的标签集合；为了应对这一挑战；许多视觉分析方法已被提出用于交互式标注；减少标注量是交互式标注的主要目标之一；为此；Moehrmann等人[32]利用自组织映射(SOM)可视化技术将相似图像聚在一起；从而允许用户一次标注多个相同类别的相似图像；这种策略也被Khayat等人[28]采用以识别具有相似异常行为的社会垃圾邮件群组；Kurzhals等人[29]将其应用于移动眼动数据分析；而Halter等人[24]则用于注释和分析电影中主要色彩策略的应用情况；除了聚类相似项外；其他策略如过滤等也已被应用来发现感兴趣的数据项以供标注；在MediaTable[36]中通过过滤和排序功能可快速定位出相似的视频片段；通过表格视图展示视频片段及其属性信息；用户可以通过过滤无关片段并根据属性排序来排列出相关片段；从而一次性标注多个同一类别中的视频片段；Stein等人[39]则提供了基于规则的过滤引擎以发现足球比赛视频中的兴趣模式；
Recently, to enhance the effectiveness of interactive labeling, various visual analytics methods have incorporated visualization techniques with machine learning approaches such as active learning. The concept of "interactive labeling" was first proposed by Höferlin et al. [26], which integrates human knowledge into active learning. Users are not only capable of querying instances and labeling them through active learning but also possess the ability to comprehend and guide machine learning models interactively. This concept is also utilized in text document retrieval [25], sequential data retrieval [30], trajectory classification [27], identifying relevant tweets [37], and argumentation mining [38]. For instance, Sperrle et al. [38] developed a language model for recommending text fragments in argumentation mining tasks, employing a layered visual abstraction to support five key analysis tasks required for text fragment annotation. Beyond developing interactive labeling systems, several empirical studies were conducted to evaluate their effectiveness. Bernard et al. [18] demonstrated the superiority of user-centered visual interactive labeling over model-centered active learning through experimental comparisons. Additionally, a quantitative analysis [19] examined user strategies for selecting samples during the labeling process, revealing that data-based approaches (e.g., clusters and dense areas) proved effective in early stages while model-based strategies (e.g., class separation) became more advantageous in later stages.
3.2 Improving feature quality
A common approach to enhancing feature quality involves selecting useful features that most significantly contribute to accurate predictions, referred to as feature selection techniques (see reference 278). A typical strategy for feature selection is identifying a subset of informative features that minimize redundancy among themselves while maximizing their relevance towards target attributes, such as instance classes [46]. Along this line, several methods have been developed to analyze and manage feature redundancy and relevance through interactive means. For instance, Seo and Shneiderman [48] introduced a framework that ranks features based on their relevance. This method employs tables and matrices to visually represent ranking results. Building on this foundation, Ingram et al. [44] developed DimStiller, a visual analytics system enabling users to explore feature relationships interactively while eliminating irrelevant or redundant features. May et al. [46] proposed SmartStripes, which allows users to select different feature subsets for various data subsets. By utilizing matrix-based layouts, they aim to display the relevance and redundancy of these features effectively. Mühlbacher and Piringer [47] advanced this concept by creating partition-based visualizations for exploring feature relevance at varying levels of detail. Tam et al. [49] employed parallel coordinates visualization to identify discriminative features across different clusters using cross-validation folds and classification models. Finally, Krause et al. [45] conducted comparative analyses across multiple feature selection algorithms, validation folds, and classification models to rank features accordingly. Users can interactively select optimal combinations of features and models for enhanced performance.
Additionally selecting existing features while constructing new ones also contributes significantly to model development. For instance FeatureInsight [279] provides a method for creating novel features specifically tailored for text classification tasks. Through visually analyzing classifier errors and identifying their underlying causes users can devise innovative solutions by developing new attributes capable of distinguishing between misclassified instances effectively. By enhancing the generalization capability of newly constructed features collective analysis through visual summaries improves accuracy across multiple error cases rather than addressing individual instances alone.
4 Techniques during model building
Machine learning models are typically perceived as black boxes due to their limited interpretability, thereby restricting their practical applications in high-stakes domains like autonomous vehicles and financial investments. Current visual analytics techniques in model construction aim to investigate how to elucidate the underlying mechanisms of machine learning models, subsequently assisting model developers in constructing well-founded models. Initially, model developers require a comprehensive understanding of models to eliminate the time-consuming trial-and-error process. When the training process encounters failure or yields unsatisfactory performance, model developers must diagnose issues arising during training. Finally, there is a growing need for assistance in model steering since a significant amount of time is invested in enhancing model performance throughout the development process. To address these requirements, researchers have developed numerous visual analytics methods to enhance model understanding, diagnosis, and steering [1,2].
4.1 Model understanding
Research efforts pertaining to model comprehension can be categorized into two main types: those investigating how parameters influence outcomes, and those analyzing how models operate.
4.1.1 Understanding the effects of parameters
从模型参数的变化中观察模型输出的变化情况是一种理解模型的方法。例如，Ferreira等人（2004）开发了BirdVis工具来探索不同参数配置与模型输出之间的关系；这些关系主要体现在鸟类发生预测的应用场景中。此外，在预测模型中，该工具还揭示了各参数之间的相互关系。张等（2018）提出了一种可视化方法来展示变量对逻辑回归模型统计指标的影响。
4.1.2 Understanding model behaviours
Another key aspect is the mechanism by which the model generates desired outputs. The three main types of methods are specifically network-centric, instance-centric, and hybrid methods. Network-centric methods aim to investigate how different parts of the model cooperate, such as neurons or layers in convolutional neural networks (CNNs), interact with each other to produce final outputs. Earlier studies have utilized directed graph layouts for visualizing neural network structures [280], yet visual clutter becomes increasingly problematic as network complexity grows. To address this challenge, Liu et al. [62] developed CNNVis, a tool that visualizes deep CNNs (see Figure 4). This tool employs clustering techniques to group neurons with similar roles and their connections, effectively reducing visual clutter caused by their large quantity. Consequently, experts can better comprehend the roles of individual neurons and their learned features, as well as how low-level features aggregate into higher-level ones through the network structure. Later research by Wongsuphasawat et al. [77] introduced a graph visualization method for exploring machine learning architectures within TensorFlow [281]. They applied various graph transformations to derive an interpretable interactive layout from a low-level dataflow graph, thereby revealing the model's high-level structure in an accessible manner.
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Figure 4 CNNVis, a network-focused visual analysis method designed to elucidate the intricate workings of deep convolutional neural networks that feature millions of neurons and extensive connections. Permission has been granted for reproduction from Reference [62], © IEEE 2017.
Instance-centric methods are designed to offer per-instance analysis and exploration, along with examining the interactions among instances. Rauber et al. [69] presented the representations learned from each neural network layer as 2D scatterplots diagrams. Users are able to identify clusters and confusion zones within these representation projections, which enables them to comprehend the structure of the representation space established by the network. Furthermore, they can analyze how this space evolves during training sessions to gain insights into the network's learning behavior. Additionally, several visual analytics techniques for RNNs employ similar instance-centric approaches. LSTMVis [73], developed by Strobelt et al., utilized parallel coordinates to depict hidden states, facilitating an examination of changes in hidden states across texts. RNNVis [65], created by Ming et al., grouped hidden state units (each representing a dimension in an RNN's hidden state vector) into memory chips and associated word clouds with words. These relationships were modeled using a bipartite graph structure, which supports sentence-level explanations within RNNs.
Hybrid methods整合上述两种方法并充分利用其优势特点，在具体实施上也有着独特的优势体现。特别地，在实例级别上进行分析时可以结合网络架构的背景信息来提升分析效果和洞察深度。这样的背景信息有助于深入理解网络的工作原理及其内在机理运作机制。在具体应用中,Hohman等[56]提出了Summit这一创新性方法,旨在识别模型预测中起到关键作用的重要神经元节点及其相互关联性特征,该方法通过嵌入视图总结类之间的激活情况以及属性图视图揭示神经元之间的重要联系特征,从而实现了对模型预测机制的关键性节点识别功能。在实际应用案例中,Kahng等[59]提出了ActiVis这一有效工具,该方法通过构建计算图展示了模型结构并利用投影视图揭示了实例、子集以及类别之间的激活关系特征,从而实现了对复杂深层神经网络行为模式的有效可视化解析与理解
In recent years, researchers have made efforts to employ surrogate explainable models to interpret model behaviors. These methods offer significant advantages by eliminating the need for users to probe into the inner workings of the model itself. Consequently, such approaches are particularly valuable for individuals with limited or no machine learning expertise. By treating the classifier as a black-box framework, Ming et al. [66] successfully extracted rule-based insights from both the input and output data of the classifier. These insights were then systematically visualized through RuleMatrix, an interactive platform that empowers practitioners to explore the extracted rules in depth, thereby enhancing the interpretability of these models. Wang et al. [75] introduced DeepVID, a tool designed to generate visual interpretations for image classifiers. Given an image of interest, a deep generative model was first utilized to produce nearby samples. These generated samples were subsequently employed to train a simpler and more interpretable model, such as a linear regression classifier, which aids in elucidating how the original model arrives at its decisions.
4.2 Model diagnosis
Visual analysis methods for diagnosing models may either be employed to evaluate the training outcomes or be utilized to assess the training process.
4.2.1 Analyzing training results
开发出多种工具用于基于性能评估分类器[81,82,86,93]。
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This advanced visual analytics platform, AEVis, is designed to analyze adversarial samples effectively.It presents diverging and merging patterns in the extracted data pathways through river-based visualization as well as highlights critical features using layer-level analysis.Reproduced with permission from Ref.[84]© IEEE 2020
4.3 Analyzing training dynamics
Recent research efforts have focused on studying the training dynamics of machine learning models. These techniques aim to assist experts in diagnosing issues that arise during the training process. For instance, DGMTracker [89] provides tools to help identify failure reasons in deep generative models by employing a blue-noise polyline sampling algorithm, which maintains both outliers and major trends in training dynamics. Additionally, it uses a credit assignment mechanism to uncover neuron interactions for better diagnosis of failure propagation. Attention has also been directed towards diagnosing reinforcement learning processes. Wang et al. [96] introduced DQNViz for analyzing deep Q-networks in Breakout games, offering an overview through line charts and stacked area charts that display changes in overall statistics during training. At a detailed level, DQNViz employs segment clustering and pattern mining algorithms to help experts detect common and suspicious patterns in event sequences from agents interacting with Q-networks. Furthermore, He et al. [87] developed DynamicsExplorer to diagnose LSTM networks trained for controlling ball-in-maze tasks. This tool visualizes ball trajectories via a trajectory variability plot and clusters them using parallel coordinates plots to quickly identify where training failures occur.
4.4 Model steering
There are two primary approaches for model steering: fine-tuning the model using human expertise, and selecting an optimal model from an ensemble of models.
4.4.1 Model refinement with human knowledge
Several visual analytics techniques have been created to allow users to be part of the model refinement process's loop, utilizing flexible interactions.
通过直观分析技术（visual analytics techniques），用户可以直接细化目标模型。
Besides direct model updates, users can also correct flaws in the results or provide extra knowledge, allowing the model to be updated implicitly to produce improved results based on human feedback. Several works have focused on incorporating user knowledge into topic models to improve their results [13,105,106,109,124,125]. For instance, Yang et al. [125] presented ReVision that allows users to steer hierarchical clustering results by leveraging an evolutionary Bayesian rose tree clustering algorithm with constraints. As shown in Fig. 6, the constraints and the clustering results are displayed with an uncertainty-aware tree-based visualization to guide the steering of the clustering results. Users can refine the constraint hierarchy by dragging. Documents are then re-clustered based on the modified constraints. Other human-in-the-loop models have also stimulated the development of visual analytic systems to support such kinds of model refinement. For instance, Liu et al. [112] proposed MutualRanker using an uncertainty-based mutual reinforcement graph model to retrieve important blogs, users, and hashtags from microblog data. It shows ranking results, uncertainty, and its propagation with the help of a composite visualization; users can examine the most uncertain items in the graph and adjust their ranking scores. The model is incrementally updated by propagating adjustments throughout the graph.
Figure 6 ReVision, a specialized visualization system incorporating a constrained hierarchical clustering algorithm and an uncertainty-aware tree-based visualization, assists users in iteratively refining hierarchical topic modeling results. This work is reproduced with permission from Reference [125], © IEEE 2020.
4.4.2 Model selection from an ensemble
另一个模型引导策略旨在从模型集合中选择最佳模型以满足特定需求。这些模型通常在聚类分析（如[102, 118, 121]）和回归分析（如[99, 103, 113, 119]）中被发现。Clustrophile 2作为一个视觉分析系统，在聚类分析中通过推荐结果帮助用户选择合适的输入特征和聚类参数。BEAMES则是一个设计用于多元模型引导与选择的回归任务中的工具。它通过改变算法及其超参数创建了一系列回归模型，并通过交互式加权数据实例和交互式特征选择与加权进一步优化这些模型性能。用户可以对这些模型进行评估，并根据不同的性能指标如残差分数和均方误差等选择最佳模型。
5 Techniques after model building
In terms of existing efforts post-model construction,the goal of visual analytics is to assist users in comprehending and deriving meaningful insights from model outputs,such as high-dimensional datasets[5 283].Given that many of these approaches are rooted indata-centric principles,the classification framework differentiates between techniques based on whether they analyze static or dynamic datasets.The temporal aspect inherent in dataset properties plays a pivotal role in shaping visualization strategies.Thus,the proposed framework divides analytical approaches into two categories:those focused on stationary dataset characteristics and those designed for temporal dynamics.A system dedicated to stationary datasets typically processes all output variables collectively while preserving their structural integrity.However,a system designed for dynamic datasets must not only interpret outcomes at individual time steps but also capture longitudinal trends through visualization techniques that highlight progression over time.The latter requires advanced modeling capabilities to identify patterns and trends within evolving datasets
5.1 Understanding static data analysis results
Our review examines approaches to comprehend static data analysis based on data type categories. Our studies predominantly focus on textual data analysis, whereas fewer studies explore the comprehension of alternative forms.
5.1.1 Textual data analysis
The field of visual text analytics has long been one of the most commonly researched areas, strongly integrating interactive display techniques with text mining methods (such as document grouping, topic analysis, and word representation) to enable users to gain deeper insights into extensive textual datasets [5].
早期的一些工作主要依赖简单的可视化手段来直接展示经典文本挖掘技术的结果。例如，在这些研究中，Görg等人开发了一种多视图可视化系统。这种系统由列表视图、集群视图、词云、网格视图和文档视图组成，并直观地展示了文档摘要、文档集群、情感分析、实体识别以及推荐分析的结果。通过结合交互式可视化与文本挖掘技术……提供了一个平滑且富有信息量的探索环境给用户。
Most later research has focused on combining well-designed interactive visualization with state-of-the-art text mining techniques, such as topic models and deep learning models, to provide deeper insights into textual data. To provide an overview of the relevant topics discussed in multiple sources, Liu et al. [159] first utilized a correlated topic model to extract topic graphs from multiple text sources. A graph matching algorithm is then developed to match the topic graphs from different sources, and a hierarchical clustering method is employed to generate hierarchies of topic graphs. Both the matched topic graph and hierarchies are fed into a hybrid visualization which consists of a radial icicle plot and a density-based node-link diagram (see Fig. 7(a)), to support exploration and analysis of common and distinctive topics discussed in multiple sources. Dou et al. [136] introduced DemographicVis to analyze different demographic groups on social media based on the content generated by users. An advanced topic model, latent Dirichlet allocation (LDA) [284], is employed to extract topic features from the corpus. Relationships between the demographic information and extracted features are explored through a parallel sets visualization [285], and different demographic groups are projected onto the two-dimensional space based on the similarity of their topics of interest (see Fig. 7(b)). Recently, some deep learning models have also been adopted because of their better performance. For example, Berger et al. [128] proposed cite2vec to visualize the latent themes in a document collection via document usage (e.g., citations). It extended a famous word2vec model, the skip-gram model [286], to generate the embedding for both words and documents by considering the citation information and the textual content together. The words are projected into a two-dimensional space using t-SNE first, and the documents are projected onto the same space, where both the document-word relationship and document-document relationships are considered simultaneously.
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Figure 7 illustrates examples of static text visualization. (a) TopicPanorama acquires topic graphs from various sources and exhibits the connections among them through graph layout, as permitted by Ref. [159] © IEEE 2014. (b) DemographicVis assesses the similarity between distinct user groups by examining their posting content, employing t-SNE projection to uncover these connections, as permitted by Ref. [136] © IEEE 2015.]
5.1.2 Other data analysis
除了文本数据外，其他类型的数据显示出了广泛的研究兴趣。其中Hong等人[146]通过将路径线视为文档以及特征视为单词的方式构建了LDA模型来进行流场分析。建模之后，在多维尺度化方法的帮助下将原始路径线及其提取出的主题映射到二维空间中，并随后生成了多个预览页面以便于展示重要主题的相关路径线。最近开发的可视化分析工具SMARTexplore[129]旨在帮助分析人员发现并解读维度间的有趣模式。为此该工具将基于表格的可视化展示与模式匹配和子空间分析紧密集成以实现这一目标
5.2 Understanding dynamic data analysis results
除了理解静态数据分析的结果外（即了解数据分析的基本情况），还应深入研究数据中隐含主题随时间变化的情况（即如何追踪主题的变化趋势）。例如，在提供社交媒体上主要公众意见概览以及这些意见随时间变化情况的前提下（即基于实时数据追踪机制），一个系统可以帮助政界人士做出及时决策（即实现政策制定的高效性）。大多数现有的研究致力于理解带有时间戳的数据语料库中的分析结果（即通过时间维度对数据进行分类与解读）。根据系统是否支持处理流式数据的情况（即是否具备实时数据处理能力），在视觉动态数据分析领域中现有的研究可以进一步分为离线分析和在线分析两类（即根据应用场景的不同进行分类）。在离线分析中，在分析过程中所有数据均可获得（即无需等待全部数据加载完毕）；而在在线分析中，则处理那些在分析过程中不断涌入的数据（即实时更新机制的应用）。
5.2.1 Offline analysis.
Offline research can be categorized based on the analysis tasks: topic-based, event-based, and trajectory-based analyses.
Analyzing the evolution of topics within a large text corpus over time represents an important subject that has garnered considerable attention. Many existing studies employ a river metaphor to depict changes within the text corpus across time. ThemeRiver [204], for instance, stands as one of the pioneering efforts, employing the river metaphor to illustrate shifts in topic volumes. To better comprehend the content evolution within a document corpus, TIARA [220,248] utilizes an LDA model [287] to extract topics and uncover their temporal dynamics. However, merely observing volume changes and content dynamics is insufficient for complex analytical tasks where users aim to explore relationships between topics and their temporal evolutions. Consequently, subsequent research has concentrated on understanding topic relationships (such as topic splitting and merging) alongside their evolving patterns over time. For example, Cui et al. [190] initially extracted topic splitting and merging patterns from a document collection by employing an incremental hierarchical Dirichlet process model [288], subsequently developing a river metaphor augmented with well-structured glyphs to visually represent these topic relationships and their dynamic temporal changes. Xu et al. [259] leveraged a topic competition model to extract dynamic competition patterns between topics and investigate opinion leaders' impacts on social media. Sun et al. [238] extended this competition model into a "coopetition" (cooperation and competition) framework to elucidate intricate interactions among evolving topics. Wang et al. [246] proposed IdeaFlow, an interactive visual analytics system designed to uncover lead-lag relationships among various social groups over time. Nevertheless, these approaches employ a flat structure for modeling topics, which constrains their applicability in big data scenarios involving large-scale text corpora. Fortunately, there are emerging efforts that integrate hierarchical topic models with interactive visualization tools to facilitate comprehension of large text corpora's primary content. For instance, Cui et al. [191] extracted sequences of topic trees through an evolutionary Bayesian rose tree algorithm [289], followed by computation of tree cuts for each tree—these cuts are then utilized to approximate topic trees and display them within a river metaphor framework, thereby revealing dynamic relationships such as topic births, deaths, splittings, and mergers.
基于事件分析的任务是揭示有序事件序列中具有语义重要性的顺序模式[149, 202 , 222 , 226] 。为了便于对大规模事件序列进行可视化探索并发现模式 ，已设计出多种可视化分析方法 。例如 ，刘等人开发了一种用于点击流数据可视分析的方法[ 2 ] 。他们通过张量模型将点击流数据转换为𝑛n维张量 ，并通过张量分解技术提取隐含线性片段（即"线"），并将这些片段划分为若干阶段 。他们将这些片段表示为分段线条状片段 ，并使用线图 metaphor 来展示不同阶段之间的变化关系 。随后 ，在解决每个阶段固定长度的问题上 ，EventThread进行了扩展[ 1 ] 。作者们提出了一种无监督阶段分析算法以有效识别事件序列中的潜在阶段。
Figure 8TextFlow is based on river metaphors to represent the lifecycle events of topics such as emergence, extinction, merger, and bifurcation. Reproduced with permission from Ref. [190], © IEEE 2011.
Other works focus on understanding movement data (e.g., GPS records) analysis results. Andrienko et al. [174] extracted movement events from trajectories and then performed spatio-temporal clustering for aggregation. These clusters are visualized using spatio-temporal envelopes to help analysts find potential traffic jams in the city. Chu et al. [189] adopted an LDA model for mining latent movement patterns in taxi trajectories. The movement of each taxi, represented by the traversed street names, was regarded as a document. Parallel coordinates were used to visualize the distribution of streets over topics, where each axis represents a topic, and each polyline represents a street. The evolution of the topics was visualized as topic routes that connect similar topics between adjacent time windows. More recently, Zhou et al. [269] treated origin-destination flows as words and trajectories as paragraphs, respectively. Therefore, a word2Vec model was used to generate the vectorized representation for each origin-destination flow. t-SNE was then employed to project the embedding of the flows into two-dimensional space, where analysts can check the distributions of the origin-destination flows and select some for display on the map. Besides directly analyzing the original trajectory data, other papers try to augment the trajectories with auxiliary information to reduce the burden on visual exploration. Kruger et al. [212] clustered destinations with DBScan and then used Foursquare to provide detailed information about the destinations (e.g., shops, university, residence). Based on the enriched data, frequent patterns were extracted and displayed in the visualization (see Fig. 9); icons on the time axis help understand these patterns. Chen et al. [186] mined trajectories from geo-tagged social media and displayed keywords extracted from the text content, helping users explore the semantics of trajectories.
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Figure 9 illustrates the semantic enhancement of trajectory data by Kruger et al. In the spatial view (top), frequent routes and destinations are identified, while temporal patterns are extracted and displayed in the temporal view (bottom). This work is reproduced with permission from Ref. [212], © IEEE 2015.
5.2.2 Online analysis
为了高效处理流数据尤其是文本流数据类型的信息提取需求，在实际应用中往往需要借助特定的方法和技术手段以实现精准的数据解析与实时反馈输出
6 Research opportunities
Despite the substantial achievements in research into visual analytics within the domain of machine learning—both in academic settings and across real-world applications—there remain significant long-term research challenges. In this context, we address and emphasize key issues while exploring emerging research avenues.
6.1 Opportunities before model building
6.1.1 Improving data quality for weakly supervised learning
Weakly supervised learning通过从存在质量问题的数据中提取特征构建模型的方法
Additionally, while the issue of imprecise data quality is prevalent in real-world applications [292], it has garnered limited focus within the domain of visual analytics.
6.1.2 Explainable feature engineering
Most existing studies aiming to enhance feature quality primarily focus on tabular or textual data derived from conventional analytical models. While these features are inherently interpretable, this simplifies the feature engineering process. Nevertheless, features extracted using deep neural networks generally outperform handcrafted ones [295,296]. Given that deep neural networks operate as black boxes, these intricate features are challenging to interpret and present several hurdles in the feature engineering process.
In this study, features are extracted using a data-driven approach, which may not adequately represent original images or videos if the dataset is biased towards certain characteristics, such as color or texture. For instance, consider a dataset containing only dark dogs and light cats; the extracted features might highlight color differences while overlooking other distinguishing features such as facial shape or ear structure. Lacking a clear understanding of these biases in feature extraction makes it challenging to address them appropriately. Therefore, an intriguing area for future research is employing interactive visualization techniques to uncover the reasons behind feature bias. The main challenge lies in quantifying the information retained or lost during feature extraction and presenting these findings in an accessible manner.
Moreover, redundancies are present in the extracted deep features [297]. The removal of redundant features can bring about multiple advantages, including reduced storage needs and enhanced generalizability [278]. Without a clear grasp of the precise meaning of each feature, it becomes challenging to determine if a particular feature is redundant. Therefore, an intriguing future research direction involves creating a visual analytics framework designed specifically for conveying feature redundancies in an understandable manner. This approach would empower experts with the ability not only to comprehend but also actively eliminate redundant elements.
6.2 Opportunities during model building
6.2.1 Online training diagnosis
Existing visual analytics tools for model diagnosis operate predominantly offline: diagnostic data is collected post-training completion. These solutions have demonstrated efficacy in identifying root causes of training failures. However, as modern machine learning models grow increasingly intricate, training durations can extend into days or weeks. Offline diagnosis significantly constrains visual analytics' ability to support real-time model development. Consequently, there exists an urgent requirement to devise online diagnostic tools that can enhance model training efficiency by enabling anomaly detection and prompt adjustments. This approach can substantially reduce time spent on trial-and-error model refinement. The primary challenge associated with online diagnosis lies in timely anomaly identification during the training process. While developing algorithms designed to detect anomalies with both accuracy and real-time capabilities remains a complex task, interactive visualization offers a promising solution for pinpointing potential errors. Unlike offline methods, which process static diagnostic data, online analysis must handle streaming information incrementally. Therefore, an innovative progressive approach is necessary to generate insightful visualizations from partial data streams during ongoing model training. Such techniques not only aid professionals in monitoring and promptly addressing issues that arise during real-time model training processes but also provide critical insights into potential problems early on
6.2.2 Interactive model refinement
Recent studies have delved into the potential of uncertainty to enhance interactive model refinement [106,112,124,125]. Various approaches exist to assign uncertainty scores to model outputs (for instance, based on confidence scores generated by classifiers), and visual cues can assist users in focusing on model outputs with high uncertainty. After user refinement, model uncertainty is recalculated, enabling iterative refinement until users are satisfied. Furthermore, additional resources can offer users more informed guidance to streamline the process of refining models effectively. However, there remains significant potential for researchers to explore interactive model refinement. One promising direction involves leveraging feedback from previous iterations to inform subsequent guidance. For example, in a clustering application, users might establish must-link or cannot-link constraints between certain data pairs, which can then be used to instruct a model to adjust its cluster assignments in intermediate results. Additionally, incorporating prior knowledge can help identify areas where refinements are needed. For instance, model outputs that conflict with established public or domain knowledge (especially in unsupervised models like nonlinear matrix factorization and latent Dirichlet allocation for topic modeling) should be addressed during the refinement process. Consequently, such knowledge-driven strategies aim to uncover unreasonable outcomes from models, empowering users to refine them by imposing constraints.
6.3 Opportunities after model building
6.3.1 Understanding multi-modal data
Existing studies in content analysis have demonstrated significant achievements in analyzing single-mode data types, such as text, images, and videos. Nevertheless, real-world scenarios frequently involve multi-mode data that integrates diverse content forms, including text, audio, and imagery. For instance, a physician examines a patient by integrating multiple data sources like medical records (text), lab reports (tables), and CT scans (images). Analyzing such multi-mode data presents challenges since simply combining insights from single-mode models fails to capture intricate relationships between different modes. Consequently, employing multi-mode machine learning methodologies and leveraging their capabilities to uncover patterns across varied data types becomes increasingly promising. To enhance understanding of these multi-mode learning models' outputs, it is imperative to develop a more robust visual analytics system that can comprehensively interpret the results. Various machine learning models have been developed to learn joint representations of multi-mode data streams [298-299]. Moving forward, exploring effective methods for visualizing these joint representations across all modes will significantly improve comprehension of the underlying data and their interconnections. Additionally, applying classic multi-mode tasks can facilitate more natural interactions in the domain of visual analytics. For example, in the vision-language integration scenario, tasks such as visual grounding (identifying specific image regions based on descriptions) can provide intuitive interfaces for image retrieval using natural language queries within visual environments.
6.3.2 Analyzing concept drift
In real-world applications, it is often assumed that the mapping from input data to output values (e.g., prediction label) is static. However, as data continues to arrive, the mapping between the input data and output values may change in unexpected ways [300]. In such a situation, a model trained on historical data may no longer work properly on new data. This usually causes noticeable performance degradation when the application data does not match the training data. Such a non-stationary learning problem over time is known as concept drift. As more and more machine learning applications directly consume streaming data, it is important to detect and analyze concept drift and minimize the resulting performance degradation [301,302]. In the field of machine learning, three main research topics, have been studied: drift detection, drift understanding, and drift adaptation. Machine learning researchers have proposed many automatic algorithms to detect and adapt to concept drift. Although these algorithms can improve the adaptability of learning models in an uncertain environment, they only provide a numerical value to measure the degree of drift at a given time. This makes it hard to understand why and where drift occurs. If the adaptation algorithms fail to improve the model performance, the black-box behavior of the adaptation models makes it difficult to diagnose the root cause of performance degradation. As a result, model developers need tools that intuitively illustrate how data distributions have changed over time, which samples cause drift, and how the training samples and models can be adjusted to overcoming such drift. This requirement naturally leads to a visual analytics paradigm where the expert interacts and collaborates in concept drift detection and adaptation algorithms by putting the human in the loop. The major challenges here are how to (i) visually represent the evolving patterns of streaming data over time and effectively compare data distributions at different points in time, and (ii) tightly integrate such streaming data visualization with drift detection and adaptation algorithms to form an interactive and progressive analysis environment with the human in the loop.
7 Conclusions
This paper has systematically analyzed the recent advancements in visual analytics techniques for machine learning. These techniques are categorized into three groups based on their analysis stages: pre-modeling, during-modeling, and post-modeling. Each category is elaborated through typical analysis tasks, and each task is illustrated with a representative set of works. By conducting a comprehensive review of existing visual analytics research in machine learning, we propose six emerging research directions, including enhancing data quality for weakly supervised learning and explainable feature engineering before model development, implementing online training diagnosis and intelligent model refinement during model building, and exploring multi-modal data understanding and concept drift analysis after model deployment. We trust this survey will offer an insightful overview of visual analytics research in machine learning, aiding comprehension of current knowledge and guiding future research endeavors.
Acknowledgements
This research is partially supported by the National Key Research and Development Program of China (Nos. 2018YFB100430 and 2019YFB1457), the National Natural Science Foundation of China (Nos. 6176A3B4C5, D5E7F8G9), TC19HFGI/JKL, The State Key Laboratory for …, Tsinghua University, and partially supported collaboration with Tsinghua-Kuaishou Institute of Future Media Data.
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