Feature Selection Algorithm Based on the Multi-Colony Fairness Model

As the world gradually transforms from the information world to the data-driven world, the areas of pattern recognition and date mining are facing more and more challenges. Feature subset selection process becomes a necessary part of big-data pattern recognition due to the data with explosive growth. Inspired by the behavior of grabbing resources in animals, the paper adds personal grabbing-resource behavior into the model of resource distribution transformed from the model of feature selection and proposes multi-colony fairness algorithm(MCFA) to deal with this behavior in order to obtain a better distribution scheme (i.e. to obtain a better feature subset). The algorithm effectively fuses the strategies of the random search and the heuristic search. In addition, it combines the methods of filter and wrapper so as to reduce the amount of calculation while improving the classification accuracy. The convergence and the effectiveness of the proposed algorithm are verified both from mathematical and experimental aspects. MCFA is compared with the other four classic feature selection algorithms SFS(sequential forward selection), SBS(sequential backward selection), SFFS(sequential floating forward selection), SBFS(sequential floating backward selection) and three mainstream feature selection algorithms RRFS(relevance-redundancy feature selection), mRMR(minimal-redundancy-maximal-relevance), ReliefF. The comparison results show that the proposed algorithm can obtain better feature subsets both in the aspects of feature subset length and the classification accuracy which indicates the efficiency and the effectiveness of the proposed algorithm.