Rough set-based decision tree construction algorithm

Authors:
Sang-Wook Han;Jae-Yearn Kim
Affiliations:
Department of Industrial Engineering, Hanyang University, Seoul, Korea;Department of Industrial Engineering, Hanyang University, Seoul, Korea
Venue:
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Year:
2007

Citing 4
Cited 0

Induction of Decision Trees

Machine Learning
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables

ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We apply rough set theory to obtain knowledge from the construction of a decision tree. Decision trees are widely used in machine learning. A variety of methods for making decision trees have been developed. Our algorithm, which compares the core attributes of objects and builds decision trees based on those attributes, represents a new type of tree construction. Experiments show that the new algorithm can help to extract more meaningful and accurate rules than other algorithms.