Robust ranking by ensembling of diverse models and assessment metrics

Abstract

We propose an ensemble of classification models formed using different assessment metrics. For a given metric, a classifier performs feature selection and combines models based on different subsets of feature variables which we call phalanxes. This first step, which employs the algorithm of phalanx formation, identifies strong and diverse subsets of feature variables. A second phase of ensembling aggregates classifiers across diverse assessment metrics. The proposed method is applied to protein homology data to mine homologous proteins, where the feature variables used for developing classifiers are various measures of similarity scores of proteins, and found robust for ranking both evolutionary close and distant homologous proteins.