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Novel weighting in single hidden layer feedforward neural networks for data classification

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journal contribution
posted on 2012-07-01, 00:00 authored by S Seifollahi, John YearwoodJohn Yearwood, Bahadorreza OfoghiBahadorreza Ofoghi
We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks.

History

Journal

Computers and mathematics with applications

Volume

64

Pagination

128-136

Location

Amsterdam, The Netherlands

Open access

  • Yes

ISSN

0898-1221

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Elsevier Ltd

Issue

2

Publisher

Elsevier

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