2つのクラスのデータセットに対して10倍のSVM分類を行う方法を示す、なんとなく説明的な例が必要です。 MATLABドキュメンテーションには1つの例しかありませんが、10倍ではありません。誰かが私を助けてくれますか?
以下は、Bioinformatics Toolboxの次の関数を使用した完全な例です。 [〜#〜] 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
出力:
ans =
0.99333
ans =
100 1
0 49
0 0
99.33%
「setosa」インスタンスが1つだけの場合の精度は「non-setosa」として誤って分類されます
[〜#〜] update [〜#〜]:SVM関数はR2013aのStatisticsツールボックスに移動しました