Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: João Paulo Papa, Andre Pagnin, Silvana Artioli Schellini, André Augusto Spadotto, Rodrigo Capobianco Guido, Moacir Ponti, Giovani Chiachia, Alexandre X. Falcão
• Keywords: Feature Selection, Gravitational Search Algorithm, ICASSP 2011, Particle Swarm Optimization, Signal Processing