Akademik Veri Yönetim Sistemi
Neural Computing and Applications, cilt.34, sa.8, ss.6427-6451, 2022 (SCI-Expanded)
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Accurate classification of high-dimensional biomedical data highly depends on the efficient recognition of the data's main features which can be used to assist diagnose related diseases. However, due to the existence of a large number of irrelevant or redundant features in biomedical data, classification approaches struggle to correctly identify patterns in data without a feature selection algorithm. Feature selection approaches seek to eliminate irrelevant and redundant features to maintain or enhance classification accuracy. In this paper, a new wrapper feature selection method is proposed based on the chimp optimization algorithm (ChOA) for biomedical data classification. The ChOA is a newly proposed metaheuristic algorithm whose capability for solving feature selection problems has not been investigated yet. Two binary variants of the ChoA are introduced for the feature selection problem. In the first approach, two transfer functions (S-shaped and V-shaped) are used to convert the continuous version of ChoA to binary. In addition to the transfer function, the crossover operator is utilized in the second approach to improve the ChOA's exploratory behavior. To validate the efficiency of the proposed approaches, five publicly available high-dimensional biomedical datasets, and a few datasets from different domains such as life, text, and image are employed. The proposed approaches were then compared with six well-known wrapper-based feature selection methods, including multi-objective genetic algorithm (GA), particle swarm optimization (PSO), Bat algorithm (BA), ant colony optimization (ACO), firefly algorithm (FA), and flower pollination (FP) algorithm, as well as two standard filter-based feature selection methods using three different classifiers. The experimental results demonstrate that the proposed approaches can effectively remove the least significant features and improve classification accuracy. The suggested wrapper feature selection techniques also outperform the GA, PSO, BA, ACO, FA, FP, and other existing methods in the terms of the number of selected genes, and classification accuracy in most cases.