Abstract
In the paper we propose a new class of modular systems for classification in the case of missing features. We incorporate the rough set theory into construction of neuro-fuzzy systems which create the modular structure. The AdaBoost algorithm is combined with the gradient algorithm to train the whole system. We illustrate the performance of our approach on typical benchmarks.
This work was supported in part by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish Ministry of Science and Higher Education (Special Research Project 2006-2009) and by science funds for 2007-2010 as research project Nr N N516 1155 33.
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Scherer, R., Korytkowski, M., Nowicki, R., Rutkowski, L. (2008). Modular Rough Neuro-fuzzy Systems for Classification. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_56
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DOI: https://doi.org/10.1007/978-3-540-68111-3_56
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