Question

I am following the example on this page : Example of 10-fold SVM classification in MATLAB.

Basically, i am following the example to execute my classification. The problem i face is that pred is always postive. It is 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),'showplot',true); 
    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 0.65(65%) and the TrainLabel is <60×1 double> and TrainVec is <60×5900 double>.

Two more qns:

1. must the values of TrainLabel be 0 and 1? is it ok if it is -1 or 1

2. TrainVec is such that each feature from a image is placed in a row. The feature from the next image is placed in the next row. Is this correct? or must each of features be placed in a different column?

Need some help on this… thanks

Answer 1

Scale the values to between 0 to 1.This will solve the problem