Parallel extreme learning machine for regression based on MapReduce

Abstract

Regression is one of the most basic problems in data mining. For regression problem, extreme learning machine (ELM) can get better generalization performance at a much faster learning speed. However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task. Through analyzing the mechanism of ELM algorithm, an efficient parallel ELM for regression is designed and implemented based on MapReduce framework, which is a simple but powerful parallel programming technique currently. The experimental results demonstrate that the proposed parallel ELM for regression can efficiently handle very large datasets on commodity hardware with a good performance on different evaluation criterions, including speedup, scaleup and sizeup.