Decision Tree Construction based on Rough Set Theory under Characteristic Relation

DOI

10.2991/iske.2007.249How to use a DOI?

Keywords

Rough set, decision tree, weighted mean roughness, characteristic relation

Abstract

Several approaches based on rough set have been proposed for constructing decision tree in complete information systems. In fact, many information systems are incomplete in practical applications. In this paper, a new algorithm, Decision Tree Construction based on Rough Set Theory under Characteristic Relation (DTCRSCR), is proposed for mining classification knowledge from incomplete information systems. Its idea is that the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as current splitting node. Experimental results show the decision trees constructed by DTCRSCR tend to have simpler structures and higher classification accuracy.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jing Song
AU  - Tianrui Li
AU  - Ying Wang
AU  - Jianhuai Qi
PY  - 2007/10
DA  - 2007/10
TI  - Decision Tree Construction based on Rough Set Theory under Characteristic Relation
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1464
EP  - 1468
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.249
DO  - 10.2991/iske.2007.249
ID  - Song2007/10
ER  -