Metaheuristic Search Based Feature Selection Methods for Classification of Cancer. (November 2021)

• Record Type: Journal Article
• Authors: Meenachi, L.; Ramakrishnan, S.
• Is Part Of: Pattern recognition. Volume 119(2021)
• Publication Date: 2021-11
• Subjects: Ant Colony Optimization -- Genetic Algorithm -- Tabu Search -- Fuzzy Rough set -- Optimal feature selection; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108079
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Usage of population and neighbourhood based metaheuristic search techniques for feature selection, Application of fuzzy rough set for evaluation of subsets of features as the objective function, and Showcasing the effectiveness of hybridized global and local feature selection. Abstract: Cancer is a cluster of diseases caused due to unusual cell growth. This paper aims to discover cancer prediction from the microarray gene expression data using the selected features. The metaheuristic search algorithms select the global and local optimal features using population and neighbourhood based algorithms. Although the ant colony optimization and genetic algorithm search for the global optimal features from the dataset entails enhanced classification, sometimes there occur some challenges in the selection of neighbourhood features. Against this background, two feature selection algorithms are proposed to hybridize tabu search, a neighbourhood based search algorithm with global optimal feature selection algorithm. Those are (1) Ant Colony Optimization and Tabu search with Fuzzy Rough set for Optimal feature selection (ACTFRO) algorithm, (2) Genetic algorithm and Tabu search with Fuzzy Rough set for Optimal feature selection (GATFRO) algorithm. The performance of proposed feature selection algorithms is assessed through a fuzzy rough nearest neighbour classifier using ten-fold cross validation. Four cancer medical datasets and one non-medical dataset are used to analyse the performance of the proposed algorithms in terms of classification accuracy, computation time, sensitivity, specificity, f-measure, receiver operation characteristics and positive predicted value. Results derived from the different performance metrics confirm that the proposed algorithms evidence effective global and local feature selection hybridization with improved results.