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Soft Computing

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01.06.2011 | Focus

Fuzzy decision tree based on fuzzy-rough technique

verfasst von: Jun-hai Zhai

Erschienen in: Soft Computing | Ausgabe 6/2011

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Abstract

Using an efficient criterion in selection of fuzzy conditional attributes (i.e. expanded attributes) is important for generation of fuzzy decision trees. Given a fuzzy information system (FIS), fuzzy conditional attributes play a crucial role in fuzzy decision making. Besides, different fuzzy conditional attributes have different influences on decision making, and some of them may be more important than the others. Two well-known criteria employed to select expanded attributes are fuzzy classification entropy and classification ambiguity, both of which essentially use the ratio of uncertainty to measure the significance of fuzzy conditional attributes. Based on fuzzy-rough technique, this paper proposes a new criterion, in which expanded attributes are selected by using significance of fuzzy conditional attributes with respect to fuzzy decision attributes. An illustrative example as well as the experimental results demonstrates the effectiveness of our proposed method.

Vorheriger Artikel Learning and backtracking in non-preemptive scheduling of tasks under timing constraints

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Titel: Fuzzy decision tree based on fuzzy-rough technique
verfasst von: Jun-hai Zhai
Publikationsdatum: 01.06.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 6/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0584-0

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