Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

Abstract

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization).