Abstract
Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed approach is performed and evaluated on artificial data sets generated by a traffic road simulator.
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Bazan, J.G., Nguyen, S.H., Nguyen, H.S., Skowron, A. (2004). Rough Set Methods in Approximation of Hierarchical Concepts. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_41
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DOI: https://doi.org/10.1007/978-3-540-25929-9_41
Publisher Name: Springer, Berlin, Heidelberg
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