Aggregating multiple classification results using fuzzy integration and stochastic feature selection

Authors:
Nick J. Pizzi;Witold Pedrycz
Affiliations:
National Research Council of Canada, Winnipeg, MB, Canada R3B 1Y6 and Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada R3T 2N2;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2N4
Venue:
International Journal of Approximate Reasoning
Year:
2010

Citing 19
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Abstract

Classifying magnetic resonance spectra is often difficult due to the curse of dimensionality; scenarios in which a high-dimensional feature space is coupled with a small sample size. We present an aggregation strategy that combines predicted disease states from multiple classifiers using several fuzzy integration variants. Rather than using all input features for each classifier, these multiple classifiers are presented with different, randomly selected, subsets of the spectral features. Results from a set of detailed experiments using this strategy are carefully compared against classification performance benchmarks. We empirically demonstrate that the aggregated predictions are consistently superior to the corresponding prediction from the best individual classifier.