MATLAB代码：机器学习-分类器

本文包含三种机器学习分类器的MATLAB实现方式代码块：支持向量机、决策树、逻辑回归。

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目录

 SVM/支持向量机(Support Vector Machine)

原理

MATLAB实现

实例代码块

采用搜索确定参数

Decision Tree / 决策树

原理

MATLAB实现

实例代码块

Logistic Regression / 逻辑回归

原理

MATLAB实现

实例代码块

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 SVM/支持向量机(Support Vector Machine) 原理

详细原理可参考：机器学习（九）：支持向量机SVM(超详细理论基础）_支持向量机的分类模型-CSDN博客

MATLAB实现 实例代码块

1- 导入数据

%% 训练集比例 train_ratio = 0.8; %可自行设置比例 %% 导入数据 load('features.mat'); n = randperm(size(features,1)); train_num = floor(train_ratio * size(features,1)); train_features = features(n(1:train_num),:); train_labels = labels(n(1:train_num),:); test_features = features(n(train_num + 1:end),:); test_labels = labels(n(train_num + 1:end),:);

2- 数据处理与设置

%% 参数设置 c = 20; %根据需要设置合适的值 g = 1.5; %根据需要设置合适的值 acc = 0; %准确率初始化 %% 数据处理 [Train_features,PS] = mapminmax(train_features'); Train_features = Train_features'; Test_features = mapminmax('apply',test_features',PS); Test_features = Test_features';

3- SVM核心代码

template = templateSVM('KernelFunction','rbf','BoxConstraint',c,'KernelScale',g); h_model = waitbar(0, '正在训练多类别SVM模型...'); %进度条显示 model = fitcecoc(Train_features,train_labels,'Learners',template,'Coding','onevsone','Verbose',0); close(h_model); save('my_trained_model.mat','model'); %保存模型

4- 后处理

%% 后处理 [predict_label_train,~] = predict(model,Train_features); train_accuracy = sum(strcmp(predict_label_train,train_labels)) / length(train_labels); % 计算训练集准确度 [predict_label_test,~] = predict(model,Test_features); test_accuracy = sum(strcmp(predict_label_test,test_labels)) / length(test_labels); % 计算测试集准确度 confusion_matrix_train = confusionmat(train_labels,predict_label_train); figure; confusionchart(confusion_matrix_train); title('训练集混淆矩阵'); saveas(gcf, 'confusion_matrix_train.png'); confusion_matrix_test = confusionmat(test_labels,predict_label_test); figure; confusionchart(confusion_matrix_test); title('测试集混淆矩阵'); saveas(gcf, 'confusion_matrix_test.png'); disp(['训练集准确度: ',num2str(train_accuracy)]); disp(['测试集准确度: ',num2str(test_accuracy)]); 采用搜索确定参数

可以采用随机搜索＋网格搜索的方式确定较优的参数值。

%% 搜索部分 %根据需要自行修改初始值和搜索范围 num_random_search = 30; bestc_random = 15; bestg_random = 2; bestacc_random = 0; bestc = 15; bestg = 0.7; bestacc = 0; h_random = waitbar(0, '正在进行随机搜索寻找较优参数...'); for k = 1:num_random_search c_random = 2 + (6 - (2)) * rand(); g_random = -3 + (2 - (-3)) * rand(); template_random = templateSVM('KernelFunction','rbf','BoxConstraint',2^c_random,'KernelScale',2^g_random); classifier_random = fitcecoc(Train_features,train_labels,'Learners',template_random,'Coding','onevsone','Verbose',0,'CrossVal','on','KFold',v); cg_random = kfoldLoss(classifier_random); if (1 - cg_random) > bestacc_random bestacc_random = 1 - cg_random; bestc_random = 2^c_random; bestg_random = 2^g_random; end waitbar(k / num_random_search, h_random, sprintf('随机搜索已完成 %.2f%%', k / num_random_search * 100)); end close(h_random); fprintf('Best c: %f\n', bestc_random); fprintf('Best g: %f\n', bestg_random); c_center = log2(bestc_random); g_center = log2(bestg_random); c_range = 5; g_range = 0.3; c_vec = linspace(c_center - c_range,c_center + c_range,10); g_vec = linspace(c_center - g_range,c_center + g_range,10); [c,g] = meshgrid(c_vec,g_vec); [m,n] = size(c); cg = zeros(m,n); eps = 10^(-4); v = 5; h_grid = waitbar(0, '正在通过网格搜索寻找最佳c/g参数...'); total_iterations_grid = m * n; current_iteration_grid = 0; for i = 1:m for j = 1:n template = templateSVM('KernelFunction','rbf','BoxConstraint',2^c(i,j),'KernelScale',2^g(i,j)); classifier = fitcecoc(Train_features,train_labels,'Learners',template,'Coding','onevsone','Verbose',0,'CrossVal','on','KFold',v); cg(i,j) = kfoldLoss(classifier); if (1 - cg(i,j)) > bestacc bestacc = 1 - cg(i,j); bestc = 2^c(i,j); bestg = 2^g(i,j); end if abs((1 - cg(i,j)) - bestacc )<=eps && bestc > 2^c(i,j) bestacc = 1 - cg(i,j); bestc = 2^c(i,j); bestg = 2^g(i,j); end current_iteration_grid = current_iteration_grid + 1; waitbar(current_iteration_grid / total_iterations_grid, h_grid, sprintf('网格搜索已完成 %.2f%%', current_iteration_grid / total_iterations_grid * 100)); end end close(h_grid);

Decision Tree / 决策树 原理

详细原理可参考：

决策树(Decision Tree)-CSDN博客

MATLAB实现 实例代码块

1-数据导入与归一化

%% 设置训练集比例 train_ratio = 0.8; %% 设置交叉验证折数 v = 5; %% 导入数据 load('features.mat'); n = randperm(size(features,1)); train_num = floor(train_ratio * size(features,1)); % 训练集 train_features = features(n(1:train_num),:); train_labels = labels(n(1:train_num),:); % 测试集 test_features = features(n(train_num + 1:end),:); test_labels = labels(n(train_num + 1:end),:); %% 数据归一化 [Train_features,PS] = mapminmax(train_features'); Train_features = Train_features'; Test_features = mapminmax('apply',test_features',PS); Test_features = Test_features';

2-参数网格搜索

%% 网格搜索：调整决策树参数（深度、最小叶子节点样本数、分裂标准等） best_depth = 180; best_minLeaf = 3; best_splitCriterion = 'gdi'; best_accuracy = 0; depth_vec = 50:10:150; minLeaf_vec = 1:10; splitCriteria = {'gdi', 'deviance'}; h_search = waitbar(0, '正在通过搜索寻找最佳决策树参数...'); for d = depth_vec for m = minLeaf_vec for sc = splitCriteria template = templateTree('MaxNumSplits', d, 'MinLeafSize', m, 'SplitCriterion', sc{1}); classifier = fitcecoc(Train_features, train_labels, 'Learners', template, 'Coding', 'onevsone', 'Verbose', 0, 'CrossVal', 'on', 'KFold', v); cg = kfoldLoss(classifier); acc = 1 - cg; if acc > best_accuracy best_accuracy = acc; best_depth = d; best_minLeaf = m; best_splitCriterion = sc{1}; end end end end close(h_search); fprintf('最佳深度: %d\n', best_depth); fprintf('最佳最小叶子节点样本数: %d\n', best_minLeaf); fprintf('最佳分裂标准: %s\n', best_splitCriterion);

3-核心代码

%% 模型 template = templateTree('MaxNumSplits', best_depth, 'MinLeafSize', best_minLeaf, 'SplitCriterion', best_splitCriterion); model = fitcecoc(Train_features, train_labels, 'Learners', template, 'Coding', 'onevsone', 'Verbose', 0); save('my_trained_model_tree.mat', 'model');

4-后处理

%% [predict_label_train, ~] = predict(model, Train_features); train_accuracy = sum(strcmp(predict_label_train, train_labels)) / length(train_labels); % 计算训练集准确度 [predict_label_test, ~] = predict(model, Test_features); test_accuracy = sum(strcmp(predict_label_test, test_labels)) / length(test_labels); % 计算测试集准确度 confusion_matrix_train = confusionmat(train_labels, predict_label_train); figure; confusionchart(confusion_matrix_train); title('训练集混淆矩阵'); saveas(gcf, 'confusion_matrix_tree2train.png'); confusion_matrix_test = confusionmat(test_labels, predict_label_test); figure; confusionchart(confusion_matrix_test); title('测试集混淆矩阵'); saveas(gcf, 'confusion_matrix_tree2test.png'); disp(['训练集准确度: ', num2str(train_accuracy)]); disp(['测试集准确度: ', num2str(test_accuracy)]);

Logistic Regression / 逻辑回归 原理

详细原理可参考：

逻辑回归（Logistic Regression）-CSDN博客

MATLAB实现 实例代码块

1-数据导入与归一化

%% 训练集比例 train_ratio = 0.85; %% 交叉验证折数 v = 5; %% 导入数据 load('features.mat'); n = randperm(size(features, 1)); train_num = floor(train_ratio * size(features, 1)); train_features = features(n(1:train_num), :); train_labels = labels(n(1:train_num), :); test_features = features(n(train_num + 1:end), :); test_labels = labels(n(train_num + 1:end), :); %% 数据归一化 [Train_features, PS] = mapminmax(train_features'); Train_features = Train_features'; Test_features = mapminmax('apply', test_features', PS); Test_features = Test_features';

2-参数网格搜索

%% 网格搜索 %lambda_range = logspace(-5, 5, 50); best_lambda = 1e-7; best_accuracy = 0; h_grid = waitbar(0, '正在进行网格搜索调整 Lambda 参数...'); for i = 1:length(lambda_range) lambda = lambda_range(i); template = templateLinear('Learner', 'logistic', 'Regularization', 'ridge', 'Lambda', lambda); cv = cvpartition(train_labels, 'KFold', v); cv_accuracy = zeros(cv.NumTestSets, 1); for j = 1:cv.NumTestSets train_idx = cv.training(j); test_idx = cv.test(j); model = fitcecoc(Train_features(train_idx, :), train_labels(train_idx), 'Learners', template, 'Coding', 'onevsone', 'Verbose', 0); predict_label = predict(model, Train_features(test_idx, :)); cv_accuracy(j) = sum(strcmp(predict_label, train_labels(test_idx))) / length(test_idx); end mean_accuracy = mean(cv_accuracy); if mean_accuracy > best_accuracy best_accuracy = mean_accuracy; best_lambda = lambda; end waitbar(i / length(lambda_range), h_grid, sprintf('Lambda 参数调整中... %.2f%%', i / length(lambda_range) * 100)); end close(h_grid); fprintf('最佳 Lambda: %f\n', best_lambda);

3-核心代码

%% 模型 template = templateLinear('Learner', 'logistic', 'Regularization', 'ridge', 'Lambda', best_lambda); h_model = waitbar(0, '正在训练 Logistic 回归模型...'); model = fitcecoc(Train_features, train_labels, 'Learners', template, 'Coding', 'onevsone', 'Verbose', 0); close(h_model); save('my_trained_model_logistic.mat', 'model');

4-后处理

%% [predict_label_train, ~] = predict(model, Train_features); train_accuracy = sum(strcmp(predict_label_train, train_labels)) / length(train_labels); % 计算训练集准确度 [predict_label_test, ~] = predict(model, Test_features); test_accuracy = sum(strcmp(predict_label_test, test_labels)) / length(test_labels); % 计算测试集准确度 confusion_matrix_train = confusionmat(train_labels, predict_label_train); figure; confusionchart(confusion_matrix_train); title('训练集混淆矩阵'); saveas(gcf, 'confusion_matrix_log2_train.png'); confusion_matrix_test = confusionmat(test_labels, predict_label_test); figure; confusionchart(confusion_matrix_test); title('测试集混淆矩阵'); saveas(gcf, 'confusion_matrix_log2_test.png'); disp(['训练集准确度: ', num2str(train_accuracy)]); disp(['测试集准确度: ', num2str(test_accuracy)]);