1998 | OriginalPaper | Buchkapitel
Classifier Systems Based on Possibility Distributions: A Comparative Study
verfasst von : S. Singh, E. L. Hines, J. W. Gardner
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
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The main aim of this paper is three fold: a) to understand the working of a classifier system based on possibility distribution functions, b) to evaluate its performance against other superior methods such as fuzzy and non-fuzzy neural networks on real data, c) and finally to recommend changes for enhancing its performance. The paper explains how to construct a possibility based classifier system which is used with conventional error-estimation techniques such as cross-validation and bootstrapping. The results were obtained on a set of electronic nose data and this performance was compared with earlier published results on the same data using fuzzy and non-fuzzy neural networks. The results show that the possibility approach is superior to the non-fuzzy approach, however, further work needs to be done.