决策树分类器的应用研究——乳腺癌诊断
发布时间
阅读量：
阅读量
决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。
%% 决策树分类器在乳腺癌诊断中的应用研究（2009a版本）
%% 清空环境变量
clear all
clc
warning off
%% 导入数据
load data.mat
% 随机产生训练集/测试集
a = randperm(569);
Train = data(a(1:500),:);
Test = data(a(501:end),:);
% 训练数据
P_train = Train(:,3:end);
T_train = Train(:,2);
% 测试数据
P_test = Test(:,3:end);
T_test = Test(:,2);
%% 创建决策树分类器
ctree = classregtree(P_train,T_train);
% 查看决策树视图
view(ctree);
%% 仿真测试
T_sim = eval(ctree,P_test);
%% 结果分析
count_B = length(find(T_train == 1));
count_M = length(find(T_train == 2));
rate_B = count_B / 500;
rate_M = count_M / 500;
total_B = length(find(data(:,2) == 1));
total_M = length(find(data(:,2) == 2));
number_B = length(find(T_test == 1));
number_M = length(find(T_test == 2));
number_B_sim = length(find(T_sim == 1 & T_test == 1));
number_M_sim = length(find(T_sim == 2 & T_test == 2));
disp(['病例总数：' num2str(569)...
' 良性：' num2str(total_B)...
' 恶性：' num2str(total_M)]);
disp(['训练集病例总数：' num2str(500)...
' 良性：' num2str(count_B)...
' 恶性：' num2str(count_M)]);
disp(['测试集病例总数：' num2str(69)...
' 良性：' num2str(number_B)...
' 恶性：' num2str(number_M)]);
disp(['良性乳腺肿瘤确诊：' num2str(number_B_sim)...
' 误诊：' num2str(number_B - number_B_sim)...
' 确诊率p1=' num2str(number_B_sim/number_B*100) '%']);
disp(['恶性乳腺肿瘤确诊：' num2str(number_M_sim)...
' 误诊：' num2str(number_M - number_M_sim)...
' 确诊率p2=' num2str(number_M_sim/number_M*100) '%']);
%% 决策树分类器在乳腺癌诊断中的应用研究（2012b版本）
%% 清空环境变量
clear all
clc
warning off
%% 导入数据
load data.mat
% 随机产生训练集/测试集
a = randperm(569);
Train = data(a(1:500),:);
Test = data(a(501:end),:);
% 训练数据
P_train = Train(:,3:end);
T_train = Train(:,2);
% 测试数据
P_test = Test(:,3:end);
T_test = Test(:,2);
%% 创建决策树分类器
ctree = ClassificationTree.fit(P_train,T_train);
% 查看决策树视图
view(ctree);
view(ctree,'mode','graph');
%% 仿真测试
T_sim = predict(ctree,P_test);
%% 结果分析
count_B = length(find(T_train == 1));
count_M = length(find(T_train == 2));
rate_B = count_B / 500;
rate_M = count_M / 500;
total_B = length(find(data(:,2) == 1));
total_M = length(find(data(:,2) == 2));
number_B = length(find(T_test == 1));
number_M = length(find(T_test == 2));
number_B_sim = length(find(T_sim == 1 & T_test == 1));
number_M_sim = length(find(T_sim == 2 & T_test == 2));
disp(['病例总数：' num2str(569)...
' 良性：' num2str(total_B)...
' 恶性：' num2str(total_M)]);
disp(['训练集病例总数：' num2str(500)...
' 良性：' num2str(count_B)...
' 恶性：' num2str(count_M)]);
disp(['测试集病例总数：' num2str(69)...
' 良性：' num2str(number_B)...
' 恶性：' num2str(number_M)]);
disp(['良性乳腺肿瘤确诊：' num2str(number_B_sim)...
' 误诊：' num2str(number_B - number_B_sim)...
' 确诊率p1=' num2str(number_B_sim/number_B*100) '%']);
disp(['恶性乳腺肿瘤确诊：' num2str(number_M_sim)...
' 误诊：' num2str(number_M - number_M_sim)...
' 确诊率p2=' num2str(number_M_sim/number_M*100) '%']);
%% 叶子节点含有的最小样本数对决策树性能的影响
leafs = logspace(1,2,10);
N = numel(leafs);
err = zeros(N,1);
for n = 1:N
t = ClassificationTree.fit(P_train,T_train,'crossval','on','minleaf',leafs(n));
err(n) = kfoldLoss(t);
end
plot(leafs,err);
xlabel('叶子节点含有的最小样本数');
ylabel('交叉验证误差');
title('叶子节点含有的最小样本数对决策树性能的影响')
%% 设置minleaf为28，产生优化决策树
OptimalTree = ClassificationTree.fit(P_train,T_train,'minleaf',28);
view(OptimalTree,'mode','graph')
% 计算优化后决策树的重采样误差和交叉验证误差
resubOpt = resubLoss(OptimalTree)
lossOpt = kfoldLoss(crossval(OptimalTree))
% 计算优化前决策树的重采样误差和交叉验证误差
resubDefault = resubLoss(ctree)
lossDefault = kfoldLoss(crossval(ctree))
%% 剪枝
[~,~,~,bestlevel] = cvLoss(ctree,'subtrees','all','treesize','min')
cptree = prune(ctree,'Level',bestlevel);
view(cptree,'mode','graph')
% 计算剪枝后决策树的重采样误差和交叉验证误差
resubPrune = resubLoss(cptree)
lossPrune = kfoldLoss(crossval(cptree))
全部评论 (0)
还没有任何评论哟~