Intelligent Approach for Enhancing Prediction Issues in Scalable Data Mining

Abstract

Support vector regression (SVR) is one of the supervised machine learning algorithms that can be exploited for prediction issues. The main enhancement issue of SVR is attempting to select a reliable parameter to assure the high performance of SVR. In this paper, the intelligent approach is based on integrating the enhanced particle swarm optimization PSO with the SVR to achieve the proper SVR parameters that are used to improve SVR performance. The enhanced PSO is performed by implementing parallelized linear time-variant acceleration coefficients (TVAC) and inertia weight (IW) of PSO, called PLTVACIW-PSO. The proposed approach is evaluated by performing the experimental comparisons of the proposed algorithm with eleven different algorithms. These comparisons are performed by applying the proposed algorithm and these algorithms to 21 different datasets varying in their scales.