A multiple feature construction method based on gravitational search algorithm

Abstract

One of the most important issues to tackle in data classification has been the existence of non-informative features in feature sets. Therefore, feature construction (FC) is an important pre-processing task to construct discriminating features from the original ones. Gravitational search algorithm (GSA) is a powerful swarm-based metaheuristic algorithm, which has been improved and adapted to represent multiple new constructed features. Most of the swarm-based algorithms entail a population of individuals while one individual would be returned as an optimal solution at the end of the process. In this paper, the solutions’ structure of GSA have been changed in a way that each individual can be considered as a part of the solution, and the final result consists of the whole population. Consequently, each individual is a constructed feature aiming to achieve a population of good features. In other words, the proposed method is a novel multiple feature construction (MFC) method based on the GSA which is called in brief GSAMFC. The experimental results on thirteen standard data sets demonstrate that the proposed GSAMFC is highly beneficial for providing suitable and small feature subsets as well as improving the classifier accuracy. The obtained results of GSAMFC and those of the competing algorithms prove the proficiency of the proposed method.