@INPROCEEDINGS { Rsds3504,
author = {Vanderpooten, Daniel and Slowinski, Roman and Krawiec, Krzysztof}, 
title = {Construction of Rough Classifiers Based on Application of a Similarity Relation}, 
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 = {23--30}, 
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 = {Rough classifier is a set of decision rules derived from lower and upper approximations of decision classes. The definition of these approximations is originally based on an indiscernibility relation in the set of objects. From a practical point of view, the use of an indiscernibility relation requires discretization of quantitative attributes which may be problematical. This is why, we are proposing to use a more natural similarity relation, called e-indiscernibility, to define rough approximation of decision classes. A special technique for defining appropriate parameters of the e-indiscernibility has been implemented. Decision rules derived from the lower approximations, considered to be certain and robust, have been compared to those of C4.5, Layered Artificial Neural Networks and Instance-Based Learning.}, 
keywords = {rough sets, similarity relation, decision rules, classifiers, classification test, }, 
}