Abstract
STRIM (Statistical Test Rule Induction Method) has been proposed for an if-then rule induction method from the decision table independently of Rough Sets theory, not utilizing the notion of the approximation and the validity of the method has also been confirmed by a simulation model for data generation and verification of induced rules. However, the previous STRIM used a plain hypothesis of the complete correspondence with rules while a real-world dataset judged by human beings often seems to obey a partial correspondence hypothesis (PCH). This paper studies STRIM incorporating the PCH and improves the previous STRIM into a new version, STRIM2, of which performance and caution for use is examined by the above simulation model incorporating PCH. STRIM2 is also applied to the real-world dataset and draws results showing interesting suggestions.
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Kato, Y., Saeki, T., Fei, J. (2018). Application of STRIM to Datasets Generated by Partial Correspondence Hypothesis. In: Fagan, D., MartÃn-Vide, C., O'Neill, M., Vega-RodrÃguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_6
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