@INPROCEEDINGS { Rsds3770,
author = {Blaszczynski, Jerzy and Slowinski, Roman}, 
title = {Incremental Induction of Satisfactory Decision Rules from Dominance Based Rough Approximations}, 
booktitle = {Proceedings of The International Workshop on Rough Sets in Knowledge Discovery and Soft Computing}, 
conference = {International Workshop on Rough Sets in Knowledge Discovery and Soft Computing}, 
pages = {40-51}, 
year = 2003, 
editor = {Skowron, Andrzej and Szczuka, Marcin}, 
abstract = {An incremental algorithm generating satisfactory decision rules and a rule post-processing techniques are presented. The rule induction algorithm is based on the Apriori algorithm. It is extended to handle preference-ordered domains of attributes (called criteria) within Variable Consistency Dominance-based Rough Set Approach. It deals, moreover, with the problem of missing values in the data set. The algorithm has been designed for medical applications which require: (i) a careful selection of the set of decision rules representing medical experience and (ii) an easy update of these decision rules because of data set evolving in time, and (iii) not only a high predictive capacity of the set of decision rules but also a thorough explanation of a proposed decision. To satisfy all these requirements, we propose an incremental algorithm for induction of a satisfactory set of decision rules and a post-processing techniques on the generated set of rules. User's preferences with respect to attributes are also taken into account. A measure of the quality of a decision rule is proposed. It is used to select the most interesting representatives in decision final set of rules.}, 
keywords = {rough set theory (RST), multiple-criteria decision support, knowledge discovery, discussion rules, incremental learning, }, 
}