title: Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm.
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.
authors: Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
authors: Mahshid Khatibi
authors: MK
authors: Bardsiri
authors: Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
authors: Mahdi
authors: M
authors: Eftekhari
authors: Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
authors: Reza
authors: R
authors: Mousavi
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D032701:Biomimetics / Q000379:methods; D003198:Computer Simulation; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*
chemical_list: D011506:Proteins
keywords: bioinformatics; classification accuracy; classifiers; genetic algorithms; multi–gene genetic programming; protein fold recognition; weighted voting