MAT之SVM：SVM之分類預測根據已有大量數據集案例，輸入已有病例的特征向量實現乳腺癌診斷高準確率預測

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代碼設計

load BreastTissue_data.mat n = randperm(size(matrix,1)); train_matrix = matrix(n(1:80),:); train_label = label(n(1:80),:);test_matrix = matrix(n(81:end),:); test_label = label(n(81:end),:);[Train_matrix,PS] = mapminmax(train_matrix'); Train_matrix = Train_matrix'; Test_matrix = mapminmax('apply',test_matrix',PS); Test_matrix = Test_matrix';[c,g] = meshgrid(-10:0.2:10,-10:0.2:10); [m,n] = size(c); cg = zeros(m,n); eps = 10^(-4); v = 5; bestc = 1; bestg = 0.1; bestacc = 0; for i = 1:m for j = 1:ncmd = ['-v ',num2str(v),' -t 2',' -c ',num2str(2^c(i,j)),' -g ',num2str(2^g(i,j))];cg(i,j) = svmtrain(train_label,Train_matrix,cmd); if cg(i,j) > bestacc bestacc = cg(i,j);bestc = 2^c(i,j);bestg = 2^g(i,j);end if abs( cg(i,j)-bestacc )<=eps && bestc > 2^c(i,j) bestacc = cg(i,j);bestc = 2^c(i,j);bestg = 2^g(i,j);end end end cmd = [' -t 2',' -c ',num2str(bestc),' -g ',num2str(bestg)]; %model = svmtrain(train_label,Train_matrix,cmd);[predict_label_1,accuracy_1] = svmpredict(train_label,Train_matrix,model); [predict_label_2,accuracy_2] = svmpredict(test_label,Test_matrix,model); result_1 = [train_label predict_label_1]; result_2 = [test_label predict_label_2];figure plot(1:length(test_label),test_label,'r-*') hold on plot(1:length(test_label),predict_label_2,'b:o') grid on legend('真實類別','預測類別') xlabel('乳腺樣本測試集樣本編號') ylabel('乳腺樣本測試集樣本類別') string = {'乳腺樣本測試集與SVM算法預測乳腺病例結果對比(RBF核函數)—Jason niu';['accuracy = ' num2str(accuracy_2(1)) '%']}; 1 % title(string)

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