libsvm java 多分类,libsvm中的多类分类

浅浅的花香味﹌ 2022-10-04 12:53 193阅读 0赞

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I'm working with libsvm and I must implement the classification for multiclasses with one versus all.

How can I do it?

Does libsvm version 2011 use this?

I think that my question is not very clear.

if libsvm don't use automatically one versus all,I will use one svm for every class, else how can i defined this parameters in the svmtrain function.

I had read README of libsvm.

解决方案

According to the official libsvm documentation (Section 7):

LIBSVM implements the "one-against-one" approach for multi-class

classification. If k is the number of classes, then k(k-1)/2

classifiers are constructed and each one trains data from two

classes.

In classification we use a voting strategy: each binary

classification is considered to be a voting where votes can be cast

for all data points x - in the end a point is designated to be in a

class with the maximum number of votes.

In the one-against-all approach, we build as many binary classifiers as there are classes, each trained to separate one class from the rest. To predict a new instance, we choose the classifier with the largest decision function value.

As I mentioned before, the idea is to train k SVM models each one separating one class from the rest. Once we have those binary classifiers, we use the probability outputs (the -b 1 option) to predict new instances by picking the class with the highest probability.

Consider the following example:

%\# Fisher Iris dataset

load fisheriris

\[~,~,labels\] = unique(species); %\# labels: 1/2/3

data = zscore(meas); %\# scale features

numInst = size(data,1);

numLabels = max(labels);

%\# split training/testing

idx = randperm(numInst);

numTrain = 100; numTest = numInst - numTrain;

trainData = data(idx(1:numTrain),:); testData = data(idx(numTrain+1:end),:);

trainLabel = labels(idx(1:numTrain)); testLabel = labels(idx(numTrain+1:end));