Feature Selection Approach based on Firefly Algorithm and Chi-square

Abstract

Dimensionality problem is a well-known challenging issue for most classifiers in which datasets have unbalanced number of samples and features. Features may contain unreliable data which may lead the classification process to produce undesirable results. Feature selection approach is considered a solution for this kind of problems. In this paperan enhanced firefly algorithm is proposed to serve as a feature selection solution for reducing dimensionality and picking the most informative features to be used in classification. The main purpose of the proposedmodel is to improve the classification accuracy through using the selected features produced from the model, thus classification errors will decrease. Modeling firefly in this research appears through simulating firefly position by cell chi-square value which is changed after every move, and simulating firefly intensity by calculating a set of different fitness functionsas a weight for each feature. K-nearest neighbor and Discriminant analysis are used as classifiers to test the proposed firefly algorithm in selecting features. Experimental results showed that the proposed enhanced algorithmbased on firefly algorithm with chi-square and different fitness functions can provide better results than others. Results showed that reduction of dataset is useful for gaining higher accuracy in classification.