Fuzzy Rough Decision Trees

• Autorzy: An S. , Shi H. , Hu Q. , Dang J.

Identyfikatory

DOI

10.3233/FI-2014-1050

• Języki publikacji: EN

Abstrakty

EN

How to evaluate features and select nodes is one of the key issues in constructing decision trees. In this work fuzzy rough set theory is employed to design an index for evaluating the quality of fuzzy features or numerical attributes. A fuzzy rough decision tree algorithm, which can be used to address classification problems described with symbolic, real-valued or fuzzy features, is developed. As node selection, split generation and stopping criterion are three main factors in constructing a decision tree, we design different techniques to determine splits with different kinds of features. The proposed algorithm can directly generate a classification tree without discretization or fuzzification of continuous attributes. Some numerical experiments are conducted and the comparative results show that the proposed algorithm is effective compared with some popular algorithms.

Słowa kluczowe

EN

fuzzy rough sets; decision tree; classification trees; splitting branches; uncertainty reasoning

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 132, nr 3
• Strony: 381--399
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

An S.

• School of Computer Science and Technology, Tianjin University, Tianjin 300072, P.R. China

autor

Shi H.

• School of Computer Science and Technology,Tianjin University, Tianjin 300072, P.R. China

autor

Hu Q.

• School of Computer Science and Technology, Tianjin University, Tianjin 300072, P.R. China

autor

Dang J.

• School of Computer Science and Technology, Tianjin University, Tianjin 300072, P.R. China

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