@INCOLLECTION { Rsds4358,
author = {Slowinski, Roman}, 
title = {Dominance-based Rough Set Approach: a Tool for Knowledge Discovery and Decision Support}, 
booktitle = {Systemy Wspomagania Decyzji, Tom 1: Pamieci Profesora Zdzislawa Pawlaka}, 
pages = {127--133}, 
publisher = {Wydawnictwo Uniwersytetu Slaskiego}, 
address = {Katowice, Poland}, 
year = 2007, 
editor = {Wakulicz-Deja, Alicja}, 
abstract = {Dominance-based Rough Set Approach (DRSA) has been proposed to handle background knowledge about ordinal evaluations of objects from a universe, and about monotonic relationships between these evaluations, e.g. "the larger the mass and the smaller the distance, the larger gravity" or "the greater the debt of a firm, the greater its risk to failure". Such a knowledge is typical for data describing various phenomena, and for data concerning multiple criteria decision making or decision under uncertainty. It appears that the Indiscernibility-based Rough Set Approach (IRSA) proposed by Pawlak involves a primitive idea of monotonicity related to scale with only two values: "presence" and "absence" of a property. This is why IRSA can be considered as binary scale, including only "presence" and "absence" of a property to certain degree. This observation leads to very natural fuzzy generalization of the membership degrees and monotonic relationships between them, without using any fuzzy connective. We show, moreover, that this generalization is a natural continuation of the ideas given by Leibniz, Frege, Boole, lukasiewicz and Pawlak. Finally, the fuzzy rough approximations to different sets can be applied to case-based reasoning. In this perspective, we propose to consider monotonicity of the type: "the more similar is y to x, the more credible is that y belongs to the same set as x".}, 
keywords = {rough sets, ordinal data, dominance-based rough set approach, decision support, granular computing, fuzzy-rough sets, case-based reasoning, }, 
}