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Extreme learning machine (ELM) is a new single hidden layer feedback neuralnetwork. The weights of the input layer and the biases of neurons in hiddenlayer are randomly generated, the weights of the output layer can beanalytically determined. ELM has been achieved good results for a large numberof classification tasks. In this paper, a new extreme learning machine calledrough extreme learning machine (RELM) was proposed. RELM uses rough set todivide data into upper approximation set and lower approximation set, and thetwo approximation sets are utilized to train upper approximation neurons andlower approximation neurons. In addition, an attribute reduction is executed inthis algorithm to remove redundant attributes. The experimental results showed,comparing with the comparison algorithms, RELM can get a better accuracy andrepeatability in most cases, RELM can not only maintain the advantages of fastspeed, but also effectively cope with the classification task forhigh-dimensional data.

Rough extreme learning machine: a new classification method based on uncertainty measure