A Rough Set Pooled Fitness Function Based Particle Swarm Optimization Algorithm using Golden Ratio Principle for Feature Selection

Abstract

Particle Swarm Optimization, a nature based stochastic evolutionary algorithm that iteratively tries to improvise the solution pertaining to a particular objective function. The problem becomes challenging if the objective function is not properly identified nor it is properly been evaluated which results in slow convergence and inability to find the optimal solution. Hence, we propose a novel rough set based particle swarm optimization algorithm using golden ratio principle for an efficient feature selection process that focusses on two objectives: First, that results in a reduced subset of features without conceding the originality of the data and the second is that yields a high optimal result. Since many subset of features might result with a meaningful solution, we have used the golden ratio principle to extract the most reduced subset with a high optimal solution. The algorithm has been tested over several benchmark datasets. The results shows that the proposed algorithm identifies a small set of features without convincing the optimal solution, thus able to achieve the stated objectives.