Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning

In this paper, a novel learning method called post-boosting using extended G-mean (PBG) is proposed for online sequential multiclass imbalance learning (OS-MIL) in neural networks. PBG is effective due to three reasons. 1) Through postadjusting a classification boundary under extended G-mean, the challenging issue of imbalanced class distribution for sequentially arriving multiclass data can be effectively resolved. 2) A newly derived update rule for online sequential learning is proposed, which produces a high G-mean for current model and simultaneously possesses almost the same information of its previous models. 3) A dynamic adjustment mechanism provided by extended G-mean is valid to deal with the unresolved challenging dense-majority problem and two dynamic changing issues, namely, dynamic changing data scarcity (DCDS) and dynamic changing data diversity (DCDD). Compared to other OS-MIL methods, PBG is highly effective on resolving DCDS, while PBG is the only method to resolve dense-majority and DCDD. Furthermore, PBG can directly and effectively handle unscaled data stream. Experiments have been conducted for PBG and two popular OS-MIL methods for neural networks under massive binary and multiclass data sets. Through the analyses of experimental results, PBG is shown to outperform the other compared methods on all data sets in various aspects including the issues of data scarcity, dense-majority, DCDS, DCDD, and unscaled data.