www.gusucode.com > stats 源码程序 matlab案例代码 > stats/DetermineQualityOfSVMClassifiersUsingEdgeExample.m
%% Select SVM Classifier Features by Comparing Test Sample Edges % The classifier edge measures the average of the classifier margins. One % way to perform feature selection is to compare test sample edges from % multiple models. Based solely on this criterion, the classifier with the % highest edge is the best classifier. %% % Load the |ionosphere| data set. % Copyright 2015 The MathWorks, Inc. load ionosphere rng(1); % For reproducibility %% % Partition the data set into training and test sets. Specify a 15% % holdout sample for testing. Partition = cvpartition(Y,'Holdout',0.15); testInds = test(Partition); % Indices for the test set XTest = X(testInds,:); YTest = Y(testInds,:); %% % |Partition| defines the data set partition. %% % Define these two data sets: % % * |fullX| contains all predictors (except the removed column of 0s). % * |partX| contains the last 20 predictors. % fullX = X; partX = X(:,end-20:end); %% % Train SVM classifiers for each predictor set. Specify the partition % definition. FullCVSVMModel = fitcsvm(fullX,Y,'CVPartition',Partition); PartCVSVMModel = fitcsvm(partX,Y,'CVPartition',Partition); FCSVMModel = FullCVSVMModel.Trained{1}; PCSVMModel = PartCVSVMModel.Trained{1}; %% % |FullCVSVMModel| and |PartCVSVMModel| are % |ClassificationPartitionedModel| classifiers. They contain the property % |Trained|, which is a 1-by-1 cell array holding a % |CompactClassificationSVM| classifier that the software trained using the % training set. %% % Estimate the test sample edge for each classifier. fullEdge = edge(FCSVMModel,XTest,YTest) partEdge = edge(PCSVMModel,XTest(:,end-20:end),YTest) %% % The edge for the classifier trained on the complete data set is greater, % suggesting that the classifier trained using all of the predictors is % better.