Abstract
The paper proposes a new ensemble of neuro-fuzzy rough set classifiers. The ensemble uses fuzzy rules derived by the Adaboost metalearning. The rules are used in an ensemble of neuro-fuzzy rough set systems to gain the ability to work with incomplete data (in terms of missing features). This feature is not common among different machine learning methods like neural networks or fuzzy systems. The systems are combined into the larger ensemble to achieve better accuracy. Simulations on a well-known benchmark showed the ability of the proposed system to perform relatively well.
This work was partly supported by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish Ministry of Science and Higher Education (Habilitation Project 2007-2010 Nr N ’N516 1155 33, Special Research Project 2006-2009, Polish-Singapore Research Project 2008-2010, Research Project 2008-2010).
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Korytkowski, M., Nowicki, R., Scherer, R. (2009). Neuro-fuzzy Rough Classifier Ensemble. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_84
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DOI: https://doi.org/10.1007/978-3-642-04274-4_84
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