Evolutionary based optimal ensemble classifiers for HIV-1 protease cleavage sites prediction
作者：
Highlights：
• Optimal ensemble framework for HIV-1 protease cleavage site prediction.
• Optimal formation of encoding-classifier pair selection by evolutionary algorithm.
• Natural selection in number of base learners and optimal data-learner mapping.
• Credibility of propsed enesmble model in cross-domain prediction.
• Experiment on benchmark data and statistical comparison with current state of art.
摘要
•Optimal ensemble framework for HIV-1 protease cleavage site prediction.•Optimal formation of encoding-classifier pair selection by evolutionary algorithm.•Natural selection in number of base learners and optimal data-learner mapping.•Credibility of propsed enesmble model in cross-domain prediction.•Experiment on benchmark data and statistical comparison with current state of art.
论文关键词：Amino acid database,Ensemble classifier,Genetic algorithm,HIV-1 protease,Protein encoding,Support vector machine
论文评审过程：Received 23 January 2018, Revised 17 April 2018, Accepted 3 May 2018, Available online 9 May 2018, Version of Record 26 May 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.05.003
