Induction of fuzzy decision trees based on fuzzy rough set techniques

Efficient criteria to select fuzzy expanded attributes are important for generation fuzzy decision trees (FDTs). Given a fuzzy information system (FIS), fuzzy expanded attributes play a crucial role in fuzzy decision making. Besides, different fuzzy expanded attributes have different influences on decision making, and some of them may be more important than the others. This paper makes an attempt to improve fuzzy decision tree by extending FDT to the fuzzy rough set theory. One of the main contributions of this paper is a new criterion to select the expanded attributes by using accuracy measure of fuzzy expanded attributes with respect to fuzzy decision attributes.

[1]  Cezary Z. Janikow,et al.  Fuzzy decision forest , 2000, PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500).

[2]  Tamás D. Gedeon,et al.  Pattern Trees Induction: A New Machine Learning Method , 2008, IEEE Transactions on Fuzzy Systems.

[3]  Ming-Yang Wang,et al.  Construct Rough Decision Forests Based on Sequentially Data Reduction , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[4]  Zhoujun Li,et al.  A Survey of Fuzzy Decision Tree Classifier Methodology , 2007, ICFIE.

[5]  Tianrui Li,et al.  Construction of Decision Trees based Entropy and Rough Sets under Tolerance Relation , 2007 .

[6]  Jun-Hai Zhai,et al.  Fuzzy decision tree based on fuzzy-rough technique , 2011, Soft Comput..

[7]  Yiyu Yao,et al.  A Comparative Study of Fuzzy Sets and Rough Sets , 1998 .

[8]  Xizhao Wang,et al.  Induction of multiple fuzzy decision trees based on rough set technique , 2008, Inf. Sci..

[9]  Xizhao Wang,et al.  Learning fuzzy rules from fuzzy samples based on rough set technique , 2007, Inf. Sci..

[10]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[11]  Yen-Liang Chen,et al.  Constructing a decision tree from data with hierarchical class labels , 2009, Expert Syst. Appl..

[12]  Jiye Liang,et al.  A new measure of uncertainty based on knowledge granulation for rough sets , 2009, Inf. Sci..

[13]  Ruying Sun,et al.  Data mining based on fuzzy rough set theory and its application in the glass identification , 2009 .

[14]  Rajen B. Bhatt,et al.  FRCT: fuzzy-rough classification trees , 2007, Pattern Analysis and Applications.

[15]  Ruying Sun,et al.  Data mining based on fuzzy rough set theory and its application in the glass identification , 2009, 2009 International Conference on Information and Automation.

[16]  Xi-Zhao Wang,et al.  A Research on the Relation Between Training Ambiguity and Generalization Capability , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[17]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .

[18]  Qiang Shen,et al.  Fuzzy-Rough Feature Significance for Fuzzy Decision Trees , 2005 .

[19]  Wei-Zhi Wu,et al.  Generalized fuzzy rough sets , 2003, Inf. Sci..

[20]  Xizhao Wang,et al.  A sample selection algorithm in fuzzy decision tree induction and its theoretical analyses , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[21]  Lin-yan Xue,et al.  Four Matching Operators of Fuzzy Decision Tree Induction , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[22]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[23]  Xi Zhao Wang,et al.  Fuzzy decision tree based on the important degree of fuzzy attribute , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[24]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..