A multi-objective gene selection for cancer diagnosis using particle swarm optimization and mutual information

• Published in: Journal of ambient intelligence and humanized computing Vol. 15; no. 11; pp. 3777 - 3793
• Main Authors: Rafie, Azar, Moradi, Parham
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Cancer; Computational Intelligence; Convergence; Datasets; Diagnosis; Engineering; Entropy; Feature selection; Gene expression; Genes; Heuristic; Lattice theory; Literature reviews; Machine learning; Medical diagnosis; Methods; Multiple objective analysis; Optimization techniques; Original Research; Pareto optimum; Particle swarm optimization; Random variables; Redundancy; Robotics and Automation; Search methods; User Interfaces and Human Computer Interaction; Gene selection; Multi-objective; Medical diagnosis; Particle swarm optimization; Cancer classification; Information theory
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-024-04853-4

Gene expression profiling for cancer diagnosis requires the identification of optimal and non-redundant gene subsets from microarray data. We present a multi-objective particle swarm optimization (PSO) approach that balances gene-class relevancy and inter-gene redundancy by integrating mutual information. Our method employs a dual-phase search strategy: an initial PSO search followed by a local search to accelerate convergence, and a subsequent Pareto front selection to extract the non-dominated gene subsets. Experiments on cancer microarray benchmark datasets demonstrate that our approach significantly enhances feature selection and diagnosis accuracy compared to existing methods. Notably, our approach incorporates a novel dual-evaluation framework and an improved particle representation scheme, which collectively enhance robustness and prevent premature convergence. These innovations ensure a comprehensive and effective gene selection process for cancer diagnosis.