Limited tolerance relation-based decision tree algorithm

Authors:
Ting-Liang Wang;Li Wang;Guo-Ping Xia;Ying-Cheng Xu
Affiliations:
School of Economics & Management, Beihang University, Beijing, China;School of Economics & Management, Beihang University, Beijing, China;School of Economics & Management, Beihang University, Beijing, China;School of Economics & Management, Beihang University, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

Citing 5
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Induction of Decision Trees

Machine Learning
Generation of Rules from Incomplete Information Systems

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
On the Extension of Rough Sets under Incomplete Information

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Good methods for coping with missing data in decision trees

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this research, we study how to generate a decision tree from dataset with unknown values, and proposed a decision tree learning algorithm (LTR-C4.5). The algorithm based on limited tolerance relation and C4.5. Algorithm LTR- C4.5 is composed by two function modules: filling the unknown values and generating a decision tree. The algorithm recursive calls the two function modules when handling incomplete training samples. The outstanding feature of LTR- C4.5 is that it doesn't demand to fill all unknown values before generating a decision tree. Some experiments are used to simulation the algorithm and compared it to other methods.