Extended Particle Swarm Optimization (EPSO) for Feature Selection of High Dimensional Biomedical Data

TL;DR

This paper introduces EPSO, an enhanced particle swarm optimization method for feature selection in high-dimensional biomedical data, demonstrating faster processing and improved classification accuracy over traditional PSO.

Contribution

The paper presents a novel EPSO model that improves search efficiency and accuracy in feature selection for gene expression data, addressing limitations of standard PSO.

Findings

EPSO reduces processing time compared to PSO.

EPSO achieves higher classification accuracy.

EPSO effectively explores feature space in high-dimensional data.

Abstract

This paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 sec) than PSO (average of 95.72 sec). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).