Abstract
This paper focuses on a rough set method to analyze human evaluation data with much ambiguity such as sensory and feeling data. In order to handle totally ambiguous and probabilistic human evaluation data, we propose a probabilistic approximation based on information gains of equivalent classes. Furthermore, we propose a two-stage method to simply extract uncertain if–then rules using decision functions of approximate regions. Finally, we applied the proposed method to practical human sensory evaluation data and examined the effectiveness of the proposed method. The result shown that our proposed rough set method is more applicable to human evaluation data.
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Nishino, T., Nagamachi, M., Tanaka, H. (2005). Variable Precision Bayesian Rough Set Model and Its Application to Human Evaluation Data. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_31
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DOI: https://doi.org/10.1007/11548669_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28653-0
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