A fast and novel approach based on grouping and weighted mRMR for feature selection and classification of protein sequence data

Abstract

The analysis of protein sequences under bioinformatics has gained wide importance in research area. Newly added protein sequences can be analysed using existing proteins and converting them into feature vector form. However, it emerges as a challenging task to deal with huge number of features obtained using sequence encoding techniques. Since all the features obtained are not actually required, a three-stage feature selection approach has been proposed. In the first stage, features are ranked and most irrelevant features are removed; in the second stage, conflicting features are grouped together; and in third stage, a fast approach based on weighted Minimum Redundancy Maximum Relevance (wMRMR) has been proposed and applied on grouped features. Different classification methods are used to analyse the performance of the proposed approach. It is observed that the proposed approach has increased classification accuracy results and reduced time consumption in comparison to the state-of-the-art methods. © 2020 Inderscience Enterprises Ltd.