Fuzzy decision tree based on fuzzy-rough technique

Authors:
Jun-hai Zhai
Affiliations:
Hebei University, Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, 071002, Baoding, Hebei, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Year:
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.