Abstract
In this paper a fuzzy rough set approach based classifier is proposed. To design this classifier, fuzzy approximation operator proposed by Zhao, Tsang and Chen 2010 [1] has been modified and new rules are proposed for classification. The fuzzy rough set based classification rules are used to predict decision class of new objects with unknown class. To extract these rules, first we build the equivalence classes, calculate the lower approximation value and then make use of a constant degree to reduce redundant attribute values. By using this concept, we design the discernibility vector, attribute value core of every object, to develop an attribute value reduction algorithm. These rules are applied on benchmark of dataset and classification accuracy is measured. Experimental results have been carried out and it shows that number of rules, training time of classifier is reduced and classification accuracy is improved on some dataset.
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References
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Singh, A., Tiwari, A., Naegi, S. (2011). Fuzzy Rough Set Approach Based Classifier. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_65
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DOI: https://doi.org/10.1007/978-3-642-27172-4_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27171-7
Online ISBN: 978-3-642-27172-4
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