Extreme Learning Machine Ensemble Classifier for Large-Scale Data

Abstract

For classification problem, extreme learning machine (ELM) can get better generalization performance at a much faster learning speed. Nevertheless, a single ELM is unstable in data classification. The Bagging-based ensemble classifier, i.e., Bagging-ELM has been studied popularly and proved to improve the performance of ELM significantly in terms of accuracy, however, it is inappropriate to deal with large-scale datasets due to the highly intensive computation. In this study, we propose a novel ELM ensemble classifier, namely b-ELM, which leverages the Bag of Little Bootstraps technique to obtain a scalable, efficient means of classification for large-scale data. Efficiency of classification is achieved as it only requires repeated training under consideration on quantities of data that can be much smaller than the original training data. Furthermore, b-ELM is suited to implementation on modern parallel and distributed computing platforms. The experimental results demonstrate that b-ELM can efficiently handle large-scale data with a good performance on prediction accuracy.