Binary Optimization Using Hybrid Owl Optimization For Biomarker Selection From Cancer Datasets

Abstract

The enhancement of medical technology generates a massive amount of disease data. For accurate disease detection, feature subset selection plays an important role in solving different classification problems. The selection of a good feature subset enhances the accuracy of the machine learning model and reduces the training time. However, it becomes tricky and challenging to identify the optimal feature subset from the high-dimensional datasets. Different hybrid models are proposed by various researchers to deal with these types of issues. The Owl optimization algorithm proves its efficiency in selecting the optimal features. Keeping an eye on the performance of this stochastic optimization, a binary version of the hybrid Owl optimization algorithm with simulating annealing is proposed to detect the biomarker features from the cancer datasets. K nearest neighbors (KNN) are used as a wrapper to find the best biomarkers. Eight benchmark datasets were employed for the proposed model’s performance evaluation. The results show that the proposed model is significantly better than binary Owl optimization, and other counterparts using various performance matrices such as accuracy, selection of best biomarkers, and computational time. The proposed model Owl-SA performs with an accuracy of 100% in the case of CNS and lymphoma datasets. And it retains the accuracy of 99.14%, 97.16%, 98.41%, 97.94%, 98.93%,98.32% for breast, colon, ALL-AML, ovarian, SRBCT, and MLL respectively.