Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

Massive data is generated as a result of technological innovations in various fields. Medical data sets often have extremely complex dimensions with limited sample sizes. The researchers face a difficult problem in classifying this high-dimensional data. We present a novel optimization approach for better feature selection in medical data classification in this research. We call this approach a centroid mutation-based Search and Rescue optimization algorithm (cmSAR) based on a k-Nearest Neighbor (kNN) classifier for disease classification. The use of cmSAR in feature selection is to find the optimal group of features that show strong separability between two classes, solving premature convergence and improves the local search ability of the SAR algorithm. We use a fuzzy logic as a logical system, which is an extension of multi-valued logic to generate a fuzzy set and apply a centroid mutation operator on it. The statistical results of cmSAR were either identical or superior to those of well-known metaheuristic algorithms, including the Slime Mould Algorithm (SMA), Particle Swarm Optimization (PSO) algorithm, Sine Cosine Algorithm (SCA), Moth-Flame Optimization (MFO) algorithm, Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and the original SAR algorithm on 15 disease data sets with different feature sizes extracted from UCI. In addition, cmSAR outperformed the other algorithms in CEC-C06 2019 single-objective benchmark functions as well as in performance evaluation metrics for classification according to Friedman test and Bonferroni-Dunn test for statistical verification. The proposed cmSAR achieved superior performance on all the medical data sets.

Automated summary

A novel optimization approach called cmSAR is proposed for better feature selection in medical data classification. Results show that cmSAR outperformed well-known metaheuristic algorithms in disease classification and benchmark functions, demonstrating superior performance on all medical data sets.