Classifier Systems Based on Possibility Distributions: A Comparative Study
نویسندگان
چکیده
1 Singh, S., Hines, E. L.and Gardner, J. W. "Classifier Systems Based on Possibility Distributions: A Comparative Study", Proc. 3rd International Conference on Artificial Neural Networks and Genetic Algorithms ICANNGA97, Norwich, UK, Wein: Springer, pp.537-540 (2-4 April, 1997) Abstract 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 crossvalidation and boot-strapping. 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.
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