NDLI: Fuzzy modeling with decision trees

Content Provider	IEEE Xplore Digital Library
Author	Drobics, M. Bodenhofer, U.
Copyright Year	2002
Description	Author affiliation: Software Competence Center, Hagenberg, Austria (Drobics, M.; Bodenhofer, U.)
Abstract	Decision trees are a well-known and widely used method for classification problems. For handling numerical attributes or even for numerical prediction, traditional decision trees based on crisp predicates are not suitable. Through the usage of fuzzy predicates for different types of attributes, not only the expressive power of decision trees can be extended, but it also allows to create models for numerical attributes in a very natural manner. We present a logical foundation for inductive learning of fuzzy decision trees. We further show how fuzzy logical inference methods can be applied with fuzzy decision trees to provide continuous output. Extending the underlying logical language with ordering-based fuzzy predicates enables as to generate not only more compact, but also more accurate, decision trees. These explanations are complemented by remarks on how the obtained results can be interpreted and altered by the user, to provide a theoretically founded method for interactive data analysis.
File Size	587540
File Format	PDF
ISBN	0780374371
ISSN	1062922X
DOI	10.1109/ICSMC.2002.1173356
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-06
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Machine learning Classification tree analysis Fuzzy sets Numerical models Fuzzy logic Data analysis Time measurement Temperature Neural networks
Content Type	Text
Resource Type	Article

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