Abstract
This study provides a comparison between the rough sets and probabilistic network algorithms in application to learning a pseudo-independent (PI) model, a type of probabilistic models hard to learn by common probabilistic learning algorithms based on search heuristics called single-link lookahead. The experimental result from this study shows that the rough sets algorithm outperforms the common probabilistic network method in learning a PI model. This indicates that the rough sets algorithm can apply to learning PI domains.
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Lee, JH. (2005). A Comparative Evaluation of Rough Sets and Probabilistic Network Algorithms on Learning Pseudo-independent Domains. 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_59
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DOI: https://doi.org/10.1007/11548669_59
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