Discriminating proteins using a novel ensemble algorithm

Decisions of multiple hypotheses are combined in ensemble learning to produce more accurate and less risky results. In this article, we present a novel ensemble machine learning approach for the development of robust thermo stable protein discrimination. But unlike widely used ensemble approaches in which bootstrapped training data are used, we keep the original data unchanged. Instead, we build an ensemble of base classifiers that each of them uses a division of features (called feature group) to predict the class label of each sample. Then, we try to learn the base classifiers' outputs (behavior) for different samples. By testing the proposed method on a well-known dataset, we show that our ensemble method is comparable in precision, recall and f-measure to the state of the art classifiers.