Evolutionary based optimal ensemble classifiers for HIV-1 protease cleavage sites prediction

Evolutionary based optimal ensemble classifiers for HIV-1 protease cleavage sites prediction •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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Expert Systems with Applications Elsevier

Evolutionary based optimal ensemble classifiers for HIV-1 protease cleavage sites prediction

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0957-4174
D.O.I.
10.1016/j.eswa.2018.05.003
Publisher site
See Article on Publisher Site

Abstract

•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.

Journal

Expert Systems with ApplicationsElsevier

Published: Nov 1, 2018

References

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