MATLAB中10倍SVM分类的示例 [英] Example of 10-fold SVM classification in MATLAB
问题描述
我需要一个描述性的示例,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,但不是10折.有人可以帮我吗?
I need a somehow descriptive example showing how to do a 10-fold SVM classification on a two class set of data. there is just one example in the MATLAB documentation but it is not with 10-fold. Can someone help me?
推荐答案
下面是一个完整的示例,使用了生物信息学工具箱中的以下功能: SVMCLASSIFY , CLASSPERF ,
Here's a complete example, using the following functions from the Bioinformatics Toolbox: SVMTRAIN, SVMCLASSIFY, CLASSPERF, 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
我们只有一个错误分类为非setosa"的"setosa"实例获得了99.33%
的准确性
we obtained 99.33%
accuracy with only one 'setosa' instance mis-classified as 'non-setosa'
更新:SVM功能已移至R2013a中的统计"工具箱
UPDATE: SVM functions have moved to Statistics toolbox in R2013a
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