Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis

Authors:
Amin Assareh;L. Gwenn Volkert
Affiliations:
Department of Computer Science, Kent State University;Department of Computer Science, Kent State University
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

Citing 12
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Editorial: Information fusion in the realm of medical applications - A bibliographic glimpse at its growing appeal

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Abstract

Fuzzy rule base classification systems have been the focus of increased attention in recent years, due to their unique capability of providing human experts with outcomes by means of linguistic rules. In the same time period classifier fusion approaches have been shown to enhance the performance of pattern recognition systems. In the present study we applied a hybrid random subspace fusion scheme that constructs a set of different fuzzy classifiers utilizing different subsets of both the feature space and the sample domain, combining the results of these classifiers using appropriate decision functions. Experimental results using two protein mass spectra datasets of ovarian cancer demonstrate the usefulness of this approach in comparison to other classifier fusion approaches.