@INCOLLECTION { Rsds841,
author = {Tsumoto, Shusaku}, 
title = {Induction of rules about complications with the use of rough sets}, 
booktitle = {Granular computing: an emerging paradigm}, 
pages = {384-397}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
year = 2001, 
isbn = {3-7908-1387-7}, 
abstract = {One of the most important problems on rule induction methods is that they cannot extract the rules that plausibly represent experts' decision processes: the induced rules are too short to represent the reasoning of domain experts. In this paper, the characteristics of experts' rules are closely examined and a new approach to extract plausible rules is introduced, which consists of the following three procedures. First, the characterization of decision attributes (given classes) is extracted from databases and the classes are classified into several groups with respect to the characterization. Then, two kinds of sub-rules, characterization rules for each group and discrimination rules for each class in the group are induced. Finally, those two parts are integrated into one rule for each decision attribute. The proposed method was evaluated on medical databases, the experimental results of which show that induced rules correctly represent experts' decision processes.}, 
keywords = {data mining, diagnostic reasoning, focusing mechanism, hierarchical rules, knowledge acquisition, knowledge discovery, medical decision support, rough inclusion, }, 
}