The application of fusion of heterogeneous meta classifiers to enhance protein fold prediction accuracy

Abstract

Protein fold prediction problem is considered as one of the most challenging tasks for molecular biology and one of the biggest unsolved problems for science. Recently, varieties of classification approaches have been proposed to solve this problem. In this study, a fusion of heterogeneous Meta classifiers namely: LogitBoost, Random Forest, and Rotation Forest is proposed to solve this problem. The proposed approach aims at enhancing the protein fold prediction accuracy by enforcing diversity among its individual members by employing divers and accurate base classifiers. Employed classifiers combined using five different algebraic combiners (combinational policies) namely: Majority voting, Maximum of Probability, Minimum of Probability, Product of Probability, and Average of probability. Our experimental results show that our proposed approach enhances the protein fold prediction accuracy using Ding and Dubchak's dataset and Dubchak et al.'s feature set better than the previous works found in the literature. © 2011 Springer Berlin Heidelberg.