Basically, i am using SVM for classificiation for images. I used Local Binary pattern for feature extraction. The problem i face is when i apply SVM the pred is always postive. It is not able to detect negative data.Although it shows me the accuracy value, but the pred label is always 1. It is a not able to detect negative data
clear all; clc; load('C:\Users\HP\Documents\MATLAB\TrainLabel'); load('C:\Users\HP\Documents\MATLAB\TrainVec'); cvFolds = crossvalind('Kfold', TrainLabel, 10); cp = classperf(TrainLabel); for i = 1:10 testIdx = (cvFolds == i); trainIdx = ~testIdx; Model = svmtrain(TrainVec(trainIdx,:), TrainLabel(trainIdx), ... 'Autoscale',true, 'Showplot',false, 'Method','QP', ... 'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1); pred = svmclassify(Model, TrainVec(testIdx,:),'Showplot',false); cp = classperf(cp, pred, testIdx); end cp.CorrectRate cp.CountingMatrix
The values for pred is [1;1;1;1;1;1] but my correctrate is 0.53(53%) and the TrainLabel is <267x1 double> and TrainVec is <267x1495 double>.
Any reason why this is so ? Need some help on it.
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hello , i am also working on the project my project is 2dpca and svm i have completed my pca for feature extraction but could not find any way to complete my svm for classification can u give me an idea of how do i proceed doing svm with feature vector