Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Abstract

Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).