Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm Online publication date: Tue, 17-Mar-2015
by Mahshid Khatibi Bardsiri; Mahdi Eftekhari; Reza Mousavi
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 11, No. 2, 2015
Abstract: In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.
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