Parallel extreme learning machine for regression based on MapReduce

Qing He

doi:10.1016/j.neucom.2012.01.040

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Parallel extreme learning machine for regression based on MapReduce

2013-02-01

141

PRE

DOI：10.1016/j.neucom.2012.01.040

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Abstract

En 中文

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. (C) 2012 Elsevier B.V. All rights reserved.

Keywords:

Data mining

Regression

ELM

MapReduce

PELM