@INPROCEEDINGS { Rsds3773,
author = {Dembczynski, Krzysztof and Pindur, Roman and Susmaga, Robert}, 
title = {Dominance-based Rough Set Classifier without Induction of Decision Rules}, 
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 = {84--95}, 
year = 2003, 
editor = {Skowron, Andrzej and Szczuka, Marcin}, 
abstract = {Rough Sets Theory is often to the task of classification and prediction, in which objects are assigned to some pre-defined decision classes. When the classes are preference-ordered, the process of classification is referred to as sorting. To deal with the specificity of sorting problems an extension of the Classic Rough Sets Approach, called the Dominance-based Rough Sets Approach, was introduced. The final results of the analysis is a set of decision rules induced from what is called rough approximation of decision classes. The main role of the induced decision rules is to discover regularities in the analysed data set, but the same rules, when combined with a particular classification method, may also be used to classify/sort new objects (i.e. to assign the objects to appropriate classes). There exist many different rule induction strategies, including induction of an exhaustive set of rules. This  strategy produces the most comprehensive knowledge base on the analyzed data set, but it requires a considerable amount of computing time, as the complexity of the process is exponential. In this paper we present a shortcut that allows classifying new objects without generating the rules. The presented approach bears some resemblance to the idea of lazy learning.}, 
keywords = {dominance-based rough set approach, multiple-criteria decision support, knowledge discovery, decision rules, }, 
}