The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers

Abstract

Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean.