Abstract
The edited k-nearest neighbor consists of the application of the k-nearest neighbor classifier with an edited training set, in order to reduce the classification error rate. This edited training set is a subset of the complete training set in which some of the training patterns are excluded. In recent works, genetic algorithms have been successfully applied to generate edited sets. In this paper we propose three improvements of the edited k-nearest neighbor design using genetic algorithms: the use of a mean square error based objective function, the implementation of a clustered crossover, and a fast smart mutation scheme. Results achieved using the breast cancer database and the diabetes database from the UCI machine learning benchmark repository demonstrate the improvement achieved by the joint use of these three proposals.
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References
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited datasets. IEEE Transactions on Systems, Man and Cybernetics 2, 408–421 (1972)
Mollineda, R.A., Sánchez, J.S., Sotoca, J.M.: Data characterization for effective prototype selection. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 27–34. Springer, Heidelberg (2005)
Kuncheva, L.I.: Fitness functions in editing k-NN reference set by genetic algorithms. Pattern Recognition 30(6), 1041–1049 (1997)
Ho, S.Y., Liu, C.C., Liu, S.: Design of an optimal nearest neigbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters 23, 1495–1503 (2002)
Cerverón, V., Ferri, F.J.: Another move toward the minimum subset: a tabu search approach to the condensed nearest neighbor rule. IEEE Transactions on System, Man, and Cybernetics-Part B: Cybernetics 31(3), 408–413 (2001)
Chen, J.-H., Chen, H.-M., Ho, S.-Y: Design of Nearest Neighbor Classifiers Using an Intelligent Multi-objective Evolutionary Algorithm. In: Zhang, C., W. Guesgen, H., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 262–271. Springer, Heidelberg (2004)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press Inc, New York (1995)
Hartigan, J.A., Wong, M.A.: Algorithm AS 163: A k-means clustering algorithm. Applied Statistics 28(1), 100–108 (1979)
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks 8(3), 694–713 (1997)
Islam, M.M., Yao, X., Murase, K.: A constructive algorithm for training cooperative neural network ensembles. IEEE Trainsactions on Neural Networks 14(4), 820–834 (2003)
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Gil-Pita, R., Yao, X. (2007). Using a Genetic Algorithm for Editing k-Nearest Neighbor Classifiers. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_114
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DOI: https://doi.org/10.1007/978-3-540-77226-2_114
Publisher Name: Springer, Berlin, Heidelberg
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