A new multi-colony fairness algorithm for feature selection

As the world gradually transforms from an information world to a data-driven world, areas of pattern recognition and data mining are facing more and more challenges. The process of feature subset selection 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, this paper adds personal grabbing-resource behavior into the model of resource allocation transformed from the model of feature selection. Multi-colony fairness algorithm (MCFA) is proposed to deal with grabbing-resource behaviors in order to obtain a better distribution scheme (i.e., to obtain a better feature subset). The algorithm effectively fuses strategies of the random search and the heuristic search. In addition, it combines methods of filter and wrapper so as to reduce the amount of calculation while improving classification accuracies. The convergence and the effectiveness of the proposed algorithm are verified both from mathematical and experimental aspects. MCFA is compared with other four classic feature selection algorithms such as sequential forward selection, sequential backward selection, sequential floating forward selection, and sequential floating backward selection and three mainstream feature selection algorithms such as relevance–redundancy feature selection, minimal redundancy–maximal relevance, and ReliefF. The comparison results show that the proposed algorithm can obtain better feature subsets both in the aspects of feature subset length which is defined as the number of features in a feature subset and the classification accuracy. The two aspects indicate the efficiency and the effectiveness of the proposed algorithm.