Tsumoto S, Tanaka H
Department of Information Medicine, Tokyo Medical and Dental University, Japan.
Proc AMIA Annu Fall Symp. 1997:198-202.
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.
为了从包括医学领域在内的数据库中发现有意义的知识，已经引入了几种规则归纳方法。然而，大多数方法都是从数据库中的所有数据中归纳规则，当有新样本产生时无法进行增量归纳。本文介绍了一种新的知识获取方法，该方法利用粗糙集技术增量地归纳概率规则，并在两个临床数据库上进行了评估。结果表明，该方法归纳出的规则与从所有数据集提取规则的普通非增量学习方法归纳出的规则相同，但前一种方法比后一种方法需要更多的计算资源。
