Rough set based approach for inducing decision trees

Authors:
Jin-Mao Wei;Shu-Qin Wang;Ming-Yang Wang;Jun-Ping You;Da-You Liu
Affiliations:
Institute of Computational Intelligence, Northeast Normal University, Changchun, Jilin 130024, PR China and Open Symbol Computation and Knowledge Engineering Laboratory of State Education, Jilin U ...;School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin 130024, PR China;Institute of Computational Intelligence, Northeast Normal University, Changchun, Jilin 130024, PR China;Institute of Computational Intelligence, Northeast Normal University, Changchun, Jilin 130024, PR China;Open Symbol Computation and Knowledge Engineering Laboratory of State Education, Jilin University, Changchun, Jilin 130024, PR China
Venue:
Knowledge-Based Systems
Year:
2007

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Abstract

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.