Rough set based approach for inducing decision trees

This paper presents a new approach for inducing decision trees based on Variable Precision Rough Set Model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, two concepts, i.e. variable precision explicit region, variable precision implicit region, and the process for inducing decision trees are introduced. The authors discuss the differences between the rough set based approaches and the fundamental entropy based method. The comparison between the presented approach and the rough set based approach and the fundamental entropy based method on some data sets from the UCI Machine Learning Repository is also reported.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Xindong Wu.  Knowledge Acquisition from Databases , 1995 .

[3]  S. K. Michael Wong,et al.  Rough Sets: Probabilistic versus Deterministic Approach , 1988, Int. J. Man Mach. Stud..

[4]  Wojciech Ziarko,et al.  Probabilistic Decision Tables in the Variable Precision Rough Set Model , 2001, Comput. Intell..

[5]  John Mingers,et al.  An Empirical Comparison of Pruning Methods for Decision Tree Induction , 1989, Machine Learning.

[6]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[7]  Jie Cheng,et al.  Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory , 1999 .

[8]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[9]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[10]  Jerzy W. Grzymala-Busse,et al.  Data mining and rough set theory , 2000, CACM.

[11]  Zbigniew W. Ras,et al.  Imprecise Concept Learning within a Growing Language , 1989, ML.

[12]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[13]  John Mingers,et al.  An Empirical Comparison of Selection Measures for Decision-Tree Induction , 1989, Machine Learning.

[14]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[15]  Marzena Kryszkiewicz.  Maintenance of Reducts in the Variable Precision Rough Set Model , 1997 .

[16]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[17]  Weiru Liu,et al.  Learning belief networks from data: an information theory based approach , 1997, CIKM '97.