Protein classification with imbalanced data.

Generally, protein classification is a multi-class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class. The proteins in one class are seen as positive examples while those outside the class are seen as negative examples. However, the imbalanced problem will arise in this case because the number of proteins in one class is usually much smaller than that of the proteins outside the class. As a result, the imbalanced data cause classifiers to tend to overfit and to perform poorly in particular on the minority class. This article presents a new technique for protein classification with imbalanced data. First, we propose a new algorithm to overcome the imbalanced problem in protein classification with a new sampling technique and a committee of classifiers. Then, classifiers trained in different feature spaces are combined together to further improve the accuracy of protein classification. The numerical experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of accuracy. The Matlab code and supplementary materials are available at http://eserver2.sat.iis.u-tokyo.ac.jp/ approximately xmzhao/proteins.html.