Machine learning multi-classifiers for peptide classification |
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Authors: | Loris Nanni Alessandra Lumini |
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Affiliation: | (1) DEIS, IEIIT – CNR, Università di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy |
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Abstract: | In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier. Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously published in the literature. |
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Keywords: | Two-class pattern recognition problem One-class classifier Fusion of classifiers |
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