A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

Abstract

This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive-Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.

Efficient feature selection of power quality events using two dimensional (2D) particle swarms

“…The core idea behind the Two-Dimensional (2D) learning approach has been briefly discussed in the following subsections for the sake of completeness. The detailed discussion about this approach can be found in [35]. Note that, 2D learning is intended to be a generalized learning algorithm for particle swarm based feature selection methods.…”

“…The other objective of this study is to investigate the efficacy of the new Two-Dimensional feature selection algorithm on the PQ events. The Two-Dimensional (2D) learning algorithm has recently been developed by the authors as a generalized feature selection wrapper [35]. The core idea of the 2D-learning approach is to embed the information about the number of features (referred to as 'cardinality') into the learning process of particle swarms.…”

“…This, therefore, has been the motivation behind the application of 2D learning to the PQ event identification problem. In particular, the present study focuses on the practical issues encountered in the feature selection of PQ events and therefore significantly differs from [35] in the following aspects:…”

Structure Selection of Polynomial NARX Models Using Two Dimensional (2D) Particle Swarms

“…The following subsections briefly describe the 2D learning approach. More details about this approach can be found in [9].…”

“…In this study, a novel 2-D particle swarm algorithm [9] has been applied for selecting the correct structure of the NARX model of the nonlinear systems. The proposed algorithm explicitly include the cardinality (i.e., the number of terms) information in to the search process by extending the dimension of classical single dimensional particle swarm algorithm.…”