TY - JOUR
T1 - Incremental Learning of Probabilistic Rules from Clinical Databases based on Rough Set Theory
AU - Tsumoto, Shusaku
AU - Tanaka, Hiroshi
PY - 1997/1/1
Y1 - 1997/1/1
N2 - Several rule induction methods have been introduced in order to discover meaningful knowledge from databases, including medical domain. However, most of the approaches induce rules from all the data in databases and cannot induce incrementally when new samples are derived. In this paper, a new approach to knowledge acquisition, which induce probabilistic rules incrementally by using rough set technique, is introduced and was evaluated on two clinical databases. The results show that this method induces the same rules as those induced by ordinary non-incremental learning methods, which extract rules from all the datasets, but that the former method requires more computational resources than the latter approach.
AB - Several rule induction methods have been introduced in order to discover meaningful knowledge from databases, including medical domain. However, most of the approaches induce rules from all the data in databases and cannot induce incrementally when new samples are derived. In this paper, a new approach to knowledge acquisition, which induce probabilistic rules incrementally by using rough set technique, is introduced and was evaluated on two clinical databases. The results show that this method induces the same rules as those induced by ordinary non-incremental learning methods, which extract rules from all the datasets, but that the former method requires more computational resources than the latter approach.
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M3 - Article
C2 - 9357616
AN - SCOPUS:0347210486
VL - 4
SP - 198
EP - 202
JO - Journal of the American Medical Informatics Association : JAMIA
JF - Journal of the American Medical Informatics Association : JAMIA
SN - 1067-5027
IS - SUPPL.
ER -