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论文类型：期刊论文

发表时间：2019-01-24

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、Scopus

卷号：325

页面范围：269-282

ISSN号：0925-2312

关键字：Extreme learning machine; Rough set; Attribute reduction; Classification; Neural network

摘要：Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated; the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and a simpler neural network structure on most data sets; RELM cannot only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data. (C) 2018 Elsevier B.V. All rights reserved.