期刊
NEUROCOMPUTING
卷 149, 期 -, 页码 224-232出版社
ELSEVIER
DOI: 10.1016/j.neucom.2014.03.076
关键词
Extreme learning machine; Large scale learning; Online sequential learning; Mapreduce; Parallel classification
资金
- National Natural Science Foundation of China [61173030, 61272181, 61272182]
- Public Science and Technology Research Funds Projects of Ocean [201105033]
- National Basic Research Program of China [2011CB302200-G]
- 863 Program [2012AA011004]
In this age of big data, analyzing big data is a very challenging problem. MapReduce is a simple, scalable and fault-tolerant data processing framework that enables us to process a massive volume of data. Many machine learning algorithms have been designed based on MapReduce, but there are only a few works related to parallel extreme learning machine (ELM) which is a fast and accurate learning algorithm. Online sequential extreme learning machine (OS-ELM) is one of improved ELM algorithms to support online sequential learning efficiently. In this paper, we first analyze the dependency relationships of matrix calculations of OS-ELM, then propose a parallel online sequential extreme learning machine (POS-ELM) based on MapReduce. POS-ELM is evaluated with real and synthetic data with the maximum number of training data 1280 K and the maximum number of attributes 128. The experimental results show that the training accuracy and testing accuracy of POS-ELM are at the same level as those of OS-ELM and ELM, and it has good scalability with regard to the number of training data and the number of attributes. Compared to original ELM and OS-ELM where the capability to process large scale data is bounded by the limitation of resources within a single processing unit, POS-ELM can deal with much larger scale data. The larger the number of training data is, the higher the speedup of POS-ELM is. It can be concluded that POS-ELM has more powerful capability than both ELM and OS-ELM for large scale learning. (C) 2014 Elsevier B.V. All rights reserved.
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