Harnessing TLBO-Enhanced Cheetah Optimizer for Optimal Feature Selection in Cancer Data

Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complex optimization problems. These basically find the solution space very efficiently, often without utilizing the gradient information, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimization algorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets. Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks by balancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitation ability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete feature selection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subset variance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches. This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the global exploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. The objective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. The proposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC), Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), and Prostate Tumor (PT). The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%, 96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively. These results validate the effectiveness of the hybrid approach in addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier.