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poster · 2019-12-10, 20:41

我需要一个说明性的示例，展示如何对两类数据集进行10倍SVM分类。 MATLAB文档中只有一个示例，但并非十折。有人能帮我吗？

回答：

这是一个完整的示例，使用了生物信息学工具箱中的以下功能： SVMTRAIN ， SVMCLASSIFY ， CLASSPERF和CROSSVALIND 。

load fisheriris %# load iris dataset groups = ismember(species,'setosa'); %# create a two-class problem %# number of cross-validation folds: %# If you have 50 samples, divide them into 10 groups of 5 samples each, %# then train with 9 groups (45 samples) and test with 1 group (5 samples). %# This is repeated ten times, with each group used exactly once as a test set. %# Finally the 10 results from the folds are averaged to produce a single %# performance estimation. k=10; cvFolds = crossvalind('Kfold', groups, k); %# get indices of 10-fold CV cp = classperf(groups); %# init performance tracker for i = 1:k %# for each fold testIdx = (cvFolds == i); %# get indices of test instances trainIdx = ~testIdx; %# get indices training instances %# train an SVM model over training instances svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ... 'Autoscale',true, 'Showplot',false, 'Method','QP', ... 'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1); %# test using test instances pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false); %# evaluate and update performance object cp = classperf(cp, pred, testIdx); end %# get accuracy cp.CorrectRate %# get confusion matrix %# columns:actual, rows:predicted, last-row: unclassified instances cp.CountingMatrix 输出：

ans = 0.99333 ans = 100 1 0 49 0 0 我们获得了99.33%准确度，其中只有一个被错误分类为“非setosa”的“ setosa”实例

更新：SVM功能已移至R2013a中的“统计”工具箱

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2019-12-10, 20:41	#1
poster 高级会员注册日期: 2019-11-21 帖子: 3,006 声望力: 66	MATLAB中10倍SVM分类的示例我需要一个说明性的示例，展示如何对两类数据集进行10倍SVM分类。 MATLAB文档中只有一个示例，但并非十折。有人能帮我吗？回答：这是一个完整的示例，使用了生物信息学工具箱中的以下功能： SVMTRAIN ， SVMCLASSIFY ， CLASSPERF和CROSSVALIND 。 load fisheriris %# load iris dataset groups = ismember(species,'setosa'); %# create a two-class problem %# number of cross-validation folds: %# If you have 50 samples, divide them into 10 groups of 5 samples each, %# then train with 9 groups (45 samples) and test with 1 group (5 samples). %# This is repeated ten times, with each group used exactly once as a test set. %# Finally the 10 results from the folds are averaged to produce a single %# performance estimation. k=10; cvFolds = crossvalind('Kfold', groups, k); %# get indices of 10-fold CV cp = classperf(groups); %# init performance tracker for i = 1:k %# for each fold testIdx = (cvFolds == i); %# get indices of test instances trainIdx = ~testIdx; %# get indices training instances %# train an SVM model over training instances svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ... 'Autoscale',true, 'Showplot',false, 'Method','QP', ... 'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1); %# test using test instances pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false); %# evaluate and update performance object cp = classperf(cp, pred, testIdx); end %# get accuracy cp.CorrectRate %# get confusion matrix %# columns:actual, rows:predicted, last-row: unclassified instances cp.CountingMatrix 输出： ans = 0.99333 ans = 100 1 0 49 0 0 我们获得了99.33%准确度，其中只有一个被错误分类为“非setosa”的“ setosa”实例更新：SVM功能已移至R2013a中的“统计”工具箱更多&回答...