Abstract
Biological data is undergoing exponential growth in both the volume and complexity. Indeed, the selection of biological features is an important step that aims to reduce the curse of dimensionality to improve prediction performance in classification systems. In this paper, we focus on protein sequence classification which constitutes an important problem in biological sciences. We represent in first a comparative study between classical filter feature selection algorithms and feature selection methods based on new correlation techniques in order to identify relevant, not redundant features. Then, we propose an improved version of Strong Relevant Algorithm for Subset Selection (STRASS) algorithm called “optimized STRASS algorithm” that uses new correlation metrics to reduce irrelevant and redundant features.
Experimental results show the effectiveness of this work. The proposed method can be applied to high-dimensional data. The final aim of this study is to select the best pairwise combination of filter feature selection method and the best classifier that enhances the accuracy of protein classification.
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Guannoni, N., Mhamdi, F., Elloumi, M. (2019). Improved Feature Selection Algorithm for Biological Sequences Classification. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_61
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DOI: https://doi.org/10.1007/978-3-030-29551-6_61
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