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Construction of neurofuzzy models for imbalanced data classification

Gao, M., Hong, X. and Harris, C. J. (2014) Construction of neurofuzzy models for imbalanced data classification. IEEE Transactions on Fuzzy Systems, 22 (6). pp. 1472-1488. ISSN 1063-6706

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To link to this item DOI: 10.1109/TFUZZ.2013.2296091

Abstract/Summary

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36488
Uncontrolled Keywords:Cross validation, forward selection, identification, leave one out, neurofuzzy model, imbalanced data sets.
Publisher:IEEE

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