S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization Algorithm in Feature Selection Problems

TL;DR

This paper compares S-shaped and V-shaped transfer functions within the Ant Lion Optimizer algorithm for feature selection, demonstrating improved classification accuracy on multiple datasets.

Contribution

It introduces six variants of ALO with different transfer functions for feature selection, enhancing exploration and accuracy over existing methods.

Findings

Proposed ALO variants outperform existing algorithms in feature selection.

Transfer functions significantly impact the effectiveness of ALO.

Selected features improve classification accuracy across datasets.

Abstract

Feature selection is an important preprocessing step for classification problems. It deals with selecting near optimal features in the original dataset. Feature selection is an NP-hard problem, so meta-heuristics can be more efficient than exact methods. In this work, Ant Lion Optimizer (ALO), which is a recent metaheuristic algorithm, is employed as a wrapper feature selection method. Six variants of ALO are proposed where each employ a transfer function to map a continuous search space to a discrete search space. The performance of the proposed approaches is tested on eighteen UCI datasets and compared to a number of existing approaches in the literature: Particle Swarm Optimization, Gravitational Search Algorithm, and two existing ALO-based approaches. Computational experiments show that the proposed approaches efficiently explore the feature space and select the most informative features, which help to improve the classification accuracy.