A Two-Dimensional (2-D) Learning Framework for Particle Swarm based Feature Selection

TL;DR

This paper introduces a novel two-dimensional learning framework for particle swarm optimization that incorporates subset size information, improving feature selection efficiency and classification accuracy across benchmarks.

Contribution

The paper presents a generalized 2D learning approach for PSO that integrates subset cardinality, adaptable to various PSO variants for enhanced feature selection.

Findings

Smaller feature subsets selected with the new approach.

Improved classification performance on benchmark datasets.

Faster run times compared to other algorithms.

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.