Abstract
In this paper, we propose a dual distance-weighted voting for KNN, which can solve the oversmoothing of increasing the neighborhood size k. The proposed classifier is compared with the other methods on ten UCI data sets. Experimental results suggest that the proposed classifier is a promising algorithm due to its satisfactory classification performance and robustness over a large value of k.
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References
Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statical Learning. Springer, New York (2001)
Dudani, S.A.: The Distance-weighted K-Nearest Neighbor Rule. IEEE Transactions on System Man and Cybernetics 6, 325–327 (1976)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://www.archive.ics.uci.edu
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. New York, NY, USA (2001)
Wu, X.D., Kumar, V., et al.: Top 10 Algorithms in Data Mining. Knowledge Information System 14, 1–37 (2008)
Zavrel.: An Empirical Re-examination of Weighted Voting for K-NN. In: Proceedings of the 7th Belgian-Dutch Conference on Machine Learning, Tilburg, pp. 139–148 (1997)
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Gou, J., Luo, M., Xiong, T. (2011). Improving K-Nearest Neighbor Rule with Dual Weighted Voting for Pattern Classification. In: Yu, Y., Yu, Z., Zhao, J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22691-5_21
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DOI: https://doi.org/10.1007/978-3-642-22691-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22690-8
Online ISBN: 978-3-642-22691-5
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