Abstract
This paper considers a decision system with a fuzzy decision attribute (with finite set of values) to account for uncertainty. A novel fuzzy classification approach using a class of decision trees is developed. A decision tree is constructed for each decision category using Coefficient of Variation Gain as the attribute selection measure. A metric based on Residual Sum of Squares (RSS) to compare the fuzzy classifier is presented. The methodology of constructing the classifier and its performance aspects are presented.
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Acknowledgments
This work is supported by Grant No. SB/FTP/ETA-0194/2014 from Science and Engineering Research Board, India.
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Hima Bindu, K., Raghavendra Rao, C. (2015). Coefficient of Variation Based Decision Tree for Fuzzy Classification. In: Ravi, V., Panigrahi, B., Das, S., Suganthan, P. (eds) Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015). Advances in Intelligent Systems and Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-27212-2_11
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