Bio-inspired meta-heuristic as feature selector in ensemble systems: A comparative analysis

Committees of classifiers, also known as ensemble systems, are composed of individual classifiers, organized in a parallel way and their output are combined in a combination method, which provides the final output of the system. In the context of these systems, feature selection methods can be used to provide different subsets of attributes for the individual classifiers, aiming to reduce redundancy among the attributes of a pattern and to increase the diversity in such systems. Since the problem of feature selection can be reduced to a search problem and that an exhaustive search for the subsets of attributes can be considered NP-hard, heuristic search can be adopted for solving this problem. This paper aims to introduce two important optimization techniques (Ant-colony and particle swarm) as a method to select attributes in an ensemble system as well as to compare their performance with Genetic Algorithm, whose research is well established in this area. These three algorithms have in common the fact that they bio-inspired meta-heuristics, since their search rules aim to simulate some aspects of the behavior of living beings.