Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/93971
Title: Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance
Authors: Wang, Lipo.
Fu, Xiuju
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2003
Source: Fu, X., & Wang, L. (2003). Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 33(3), 399-409.
Series/Report no.: IEEE transactions on systems, man, and cybernetics – Part B: cybernetics
Abstract: For high dimensional data, if no preprocessing is carried out before inputting patterns to classifiers, the computation required may be too heavy. For example, the number of hidden units of a radial basis function (RBF) neural network can be too large. This is not suitable for some practical applications due to speed and memory constraints. In many cases, some attributes are not relevant to concepts in the data at all. In this paper, we propose a novel separability-correlation measure (SCM) to rank the importance of attributes. According to the attribute ranking results, different attribute subsets are used as inputs to a classifier, such as an RBF neural network. Those attributes that increase the validation error are deemed irrelevant and are deleted. The complexity of the classifier can thus be reduced and its classification performance improved. Computer simulations show that our method for attribute importance ranking leads to smaller attribute subsets with higher accuracies compared with the existing SUD and Relief-F methods. We also propose a modified method for efficient construction of an RBF classifier. In this method we allow for large overlaps between clusters corresponding to the same class label. Our approach significantly reduces the structural complexity of the RBF network and improves the classification performance.
URI: https://hdl.handle.net/10356/93971
http://hdl.handle.net/10220/8196
DOI: 10.1109/TSMCB.2003.810911
Rights: © 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSMCB.2003.810911].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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