A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization

TL;DR

This paper introduces a new feature selection algorithm combining Rough set theory with Bee Colony Optimization, specifically designed for medical datasets, and demonstrates its effectiveness over existing methods.

Contribution

The paper presents a novel hybrid feature selection method using Rough set theory and Bee Colony Optimization tailored for medical data analysis.

Findings

Outperforms Quick Reduct and Entropy Based Reduct in medical datasets.

Achieves more minimal and relevant feature subsets.

Demonstrates superiority over GA, ACO, and PSO hybrid methods.

Abstract

Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt to combat this. This proposed work is applied in the medical domain to find the minimal reducts and experimentally compared with the Quick Reduct, Entropy Based Reduct, and other hybrid Rough Set methods such as Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).