Abstract
The aim of this paper is to introduce a new supervised fuzzy classification methodology (FuSCa) to improve the performance of k-NN (k-Nearest Neighbor) algorithm based on the weighted nearest neighbor membership and global membership derived from the training dataset. In this classification method, the test object is assigned a class label having the maximum membership value for that corresponding class while a weighted membership vector is found after utilizing the Global and Nearest-Neighbor fuzzy membership vectors along with a global weight and a k-close weight respectively. FuSCa is compared with other approaches using the standard benchmark data-sets and found to produce better classification accuracy.
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Das, P., SivaSathya, S., Joshil Raj, K. (2014). FuSCa: A New Weighted Membership Driven Fuzzy Supervised Classifier. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_42
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DOI: https://doi.org/10.1007/978-3-319-07353-8_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
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