A Tunable Particle Swarm Size Optimization Algorithm for Feature Selection

TL;DR

This paper introduces TPSO, a novel particle swarm optimization algorithm with a tunable swarm size that dynamically adjusts based on data, enhancing feature selection and classifier accuracy.

Contribution

It presents a real-time, data-driven reconfiguration of swarm size in PSO for feature selection, improving classifier performance over traditional methods.

Findings

TPSO outperforms standard PSO in selecting relevant features.

Experimental results show improved classification accuracy.

Wilcoxon test confirms statistical significance of results.

Abstract

Feature selection is the process of identifying statistically most relevant features to improve the predictive capabilities of the classifiers. To find the best features subsets, the population based approaches like Particle Swarm Optimization(PSO) and genetic algorithms are being widely employed. However, it is a general observation that not having right set of particles in the swarm may result in sub-optimal solutions, affecting the accuracies of classifiers. To address this issue, we propose a novel tunable swarm size approach to reconfigure the particles in a standard PSO, based on the data sets, in real time. The proposed algorithm is named as Tunable Particle Swarm Size Optimization Algorithm (TPSO). It is a wrapper based approach wherein an Alternating Decision Tree (ADT) classifier is used for identifying influential feature subset, which is further evaluated by a new objective function which integrates the Classification Accuracy (CA) with a modified F-Score, to ensure better classification accuracy over varying population sizes. Experimental studies on bench mark data sets and Wilcoxon statistical test have proved the fact that the proposed algorithm (TPSO) is efficient in identifying optimal feature subsets that improve classification accuracies of base classifiers in comparison to its standalone form.