What is ReleifF

A novel evolutionary-based feature selection model for ACPs identification that will explore the relationships hidden across the various feature descriptors is explored in this chapter. In this model, the authors amalgamate the nine feature descriptors from the three groups of peptide feature descriptors including amino acid composition (three descriptors), grouped amino acid composition and composition/transition/distribution (three descriptors). The proposed model integrates these features to unfold the hidden association between the diverse features in peptide classification. However, the inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Hence, evolutionary-based feature selection is utilized in the model that involves a combination of search and feature utility estimation by ReliefF score. Through extensive experiments on benchmark dataset, it is demonstrated that the proposed model achieves improved performance.