Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Variable precision rough set model
Journal of Computer and System Sciences
Elements of machine learning
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Machine Learning
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Diagnostic Reasoning from the Viewpoint of Rough Sets
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Interpreting Low and High Order Rules: A Granular Computing Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
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One of the most important problems on rule induction methods is that they cannot extract rules, which plausibly represent experts' decision processes. On one hand, rule induction methods induce probabilistic rules, the description length of which is too short, compared with the experts' rules. On the other hand, construction of Bayesian networks generates too lengthy rules. 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.