@INPROCEEDINGS { Rsds3550,
author = {Nguyen, Trong Dung and Ho, Tu Bao}, 
title = {A Rough Sets Based Measure For Attribute Selection in Decision Tree Induction}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {339--343}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {In this paper we propose a new measure (R-measure) of dependency between groups of attributes in a data set, inspired by the notion of dependency of attribute in the rough set theory. We then apply R-measure to the problem of attribute selection in decision tree induction. We carry out carefully an experimental comparative evaluation of decision tree inductions. We carry out carefully an experimental comparative evaluation of decision tree systems using R-measure and other popular attribute selection measure: gain-index , gini-index, d distance, "chi-square".}, 
keywords = {attribute dependency measure, rough set theory (RST), inductive concept learning, decision tree, }, 
}