Threshold rules decision list.

Marcin Blachnik1, Wlodzislaw Duch2,3, and Tadeusz Wieczorek1
1Division of Computer Studies, Department of Electrotechnology, The Silesian University of Technology, Katowice, Poland;
2School of Computer Engineering, Nanyang Technological University, Singapore,
3Department of Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Torun, Poland.


Understanding data is one of most important problems. Popular crisp logic rules are easy to understand and compare, however for some datasets the number of extracted rules is very large, what affect reduction of generalization and makes the system less transparent. Another solution are fuzzy logic rules, which are much more flexible, however they don’t support symbolic and nominal attributes. Alternative systems for rules extraction base on prototype rules, this type of rules drives from similarity base learning. Presented threshold rules algorithm extracts form data small number of ordered rules, which are very accurate. Numerical experiments on real data show the usefulness of such approach as an alternative to neurofuzzy models.
Keywords: artificial intelligence, expert systems, decision support, knowledge discovery.

Reference: Blachnik M, Duch W, Wieczorek T, Threshold rules decision list. In: T. Burczyński et al. (Eds), Methods of Artificial Intelligence, AI-METH Series, Gliwice, Poland 2005, pp. 23-24.

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