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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Studies on the Necessary Data Size for Rule Induction by STRIM
verfasst von : Yuichi Kato, Tetsuro Saeki, Shoutarou Mizuno
Erschienen in: Rough Sets and Knowledge Technology
Verlag: Springer Berlin Heidelberg
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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by a simulation experiment specifying rules in advance, and by comparison with the conventional methods. However, there remains scope for future studies. One aspect which needs examination is determination of the size of the dataset needed for inducting true rules by simulation experiments, since finding statistically significant rules is the core of the method. This paper examines the theoretical necessary size of the dataset that STRIM needs to induct true rules with probability
w
[%] in connection with the rule length, and confirms the validity of this study by a simulation experiment at the rule length 2. The results provide useful guidelines for analyzing real-world datasets.