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 is evaluated on medical databases, the experimental results of which show that induced rules correctly represent experts’ decision processes.
CITATION STYLE
Tsumoto, S. (1997). Extraction of experts’ decision process from clinical databases using rough set model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1263, pp. 58–67). Springer Verlag. https://doi.org/10.1007/3-540-63223-9_106
Mendeley helps you to discover research relevant for your work.