CODEWITHSUNDEEP
matlabselectiongenetic-algorithmsvmlibsvm
lakshmen
lakshmen

Reputation: 29064

Genetic Algorithm After SVM

I have already applied SVM using LIBSVM. Now i would like to implement Genetic Algorithm for feature selection. Tried to google for some information

1) Saw this website : http://www.scribd.com/doc/31235552/Genetic-Algorithm-Implementation-Using-Matlab

2) GA Examples in MATLAB : http://www.mathworks.com/help/toolbox/gads/f6691.html

Have few questions on them

Q1) [x fval] = ga(@fitnessfun, nvars, options). This is the function to do gasolver. What should be the fitnessfun? In most ga, it is a polynomial function. But in the case of SVM, what shld be the fitnessfun?

Q2) is there any concrete examples for GA after SVM?

Like to hear some feedback.

Thanks in advance.

Upvotes: 2

Views: 3223

Answers (1)

levesque
levesque

Reputation: 8926

If you want to do feature selection, I think you have it backwards. You should run the GA for feature selection before the training of your SVM. Your fitness function could become the performance of a newly trained SVM on selected features, it depends on what you want to accomplish. Can't say you were very clear on this topic.

To answer your second comment:

There are many parts, I don't know this ga function you are using, but if you take a look at the documentation they must tell you somewhere what parameters this fitnessfun should be expecting. I'm guessing the individual for which you want to evaluate fitness is the main parameter for this function. If you evolve a selection of features, this individual would be an array of Boolean variables where true indicates a feature that is selected an false indicates a feature that is not selected. This fitness function needs to return an indicator of how well this selection of features fares, i.e. it must return a higher number for a better selection, and a lower number for a worst selection. Prediction accuracy might be a good value for this (nb. of correct predictions divided by the total number of samples).

I'm going to assume you know how to calculate the prediction accuracy of an SVM model given a dataset and its labels. Since you have a pre-trained SVM it might be a bit tricky to use it only for selected features, and it depends strongly upon the implementation of your SVM. If it is a linear SVM, you could just set the values of the non-selected features to zero in the data matrix. However, if it is an RBF SVM that won't work. You will need to understand the inner mechanisms of the SVM implementation you are relying on. I suggest making a simple example where you train an SVM on 3d data and then adapt it to work on 2d data. It strongly depends on the implementation of your SVM model.

Upvotes: 1

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