An improved particle swarm optimization for feature selection

Abstract

Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems, which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.

Optimal Feature Selection Algorithm for High Dimensional Data Sets Using Particle Swarm Optimization

“…Chuang et al [26] developed a strategy for gbest in PSO for feature selection in which gbest will be reset to zero if it maintains the same value after several iterations. Liu et al [27] introduced a multi swarm PSO algorithm to search for the optional feature subset and optimize the parameters of SVM simultaneously. Based on PSO, Unler et al [28] propose a feature selection algorithm with an adoptive selection strategy where a feature is chosen, not only ascending to the likelihood calculated by PSO but also to its contribution to the features already selected.…”

“…PSO is a population based stochastic optimization technique. It stimulates the social behavior of organisms such as bird flocking and fish schooling to describe an automatically evolving system.PSO has proved to be a very efficient technique in several fields including feature selection [6,7,8] . It is cost effective and converge more quickly when compared with other EC algorithms such as genetic algorithm (GAs) and genetic programming (GP).…”

Data feature selection based on Artificial Bee Colony algorithm

“…Suguna and Thanushkodi [23] proposed a rough set approach with ABC algorithm for dimensionality reduction using different medical data sets in the area of Dermatology for tests, whereas Shokouhifar and Sabet [24] employed the same algorithm (ABC) for feature selection using neural networks. Particle Swarm Optimization has been proposed for feature selection either as filter method [15] or as wrapper method [25][26][27]. Nakamura et al [2] proposed a wrapper method using a BAT algorithm with OPF classifier.…”

Feature Selection on Classification of Medical Datasets based on Particle Swarm Optimization

“…Each particle has its objective function value which is decided by a fitness function. The particles fly in the search space with a velocity adjusted by each particle own flying memory and companion's flying experience [8].…”