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Title of Journal: Int J Mach Learn Cyber

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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DOI

10.1007/978-3-319-42451-4_8

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ISSN

1868-808X

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Two swarm intelligence approaches for tuning extre

Authors: Abobakr Khalil Alshamiri Alok Singh Bapi Raju Surampudi
Publish Date: 2017/03/03
Volume: 9, Issue: 8, Pages: 1271-1283
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Abstract

Extreme learning machine ELM is a new algorithm for training singlehidden layer feedforward neural networks which provides good performance as well as fast learning speed ELM tends to produce good generalization performance with large number of hidden neurons as the input weights and hidden neurons biases are randomly initialized and remain unchanged during the learning process and the output weights are analytically determined In this paper two swarm intelligence based metaheuristic techniques viz Artificial Bee Colony ABC and Invasive Weed Optimization IWO are proposed for tuning the input weights and hidden biases The proposed approaches are called ABCELM and IWOELM in which the input weights and hidden biases are selected using ABC and IWO respectively and the output weights are computed using the MoorePenrose MP generalized inverse The proposed approaches are tested on different benchmark classification data sets and simulations show that the proposed approaches obtain good generalization performance in comparison to the other techniques available in the literature

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