Feature selection approach based on whale optimization algorithm

In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.

[1]  Li-Yeh Chuang,et al.  Improved binary PSO for feature selection using gene expression data , 2008, Comput. Biol. Chem..

[2]  Aboul Ella Hassanien,et al.  Firefly Optimization Algorithm for Feature Selection , 2015, BCI.

[3]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[4]  Aboul Ella Hassanien,et al.  Hybrid Monkey Algorithm with Krill Herd Algorithm optimization for feature selection , 2015, 2015 11th International Computer Engineering Conference (ICENCO).

[5]  Crina Grosan,et al.  Feature Subset Selection Approach by Gray-Wolf Optimization , 2014, AECIA.

[6]  Cheng-Lung Huang,et al.  A distributed PSO-SVM hybrid system with feature selection and parameter optimization , 2008, Appl. Soft Comput..

[7]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[8]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[9]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[10]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[11]  Mengjie Zhang,et al.  Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms , 2014, Appl. Soft Comput..

[12]  Aboul Ella Hassanien,et al.  Hybrid flower pollination algorithm with rough sets for feature selection , 2015, 2015 11th International Computer Engineering Conference (ICENCO).