Abstract
Since kNN classifiers are sensitive to outliers and noise contained in the training data set, many approaches have been proposed to edit the training data so that the performance of the classifiers can be improved. In this paper, through detaching the two schemes adopted by the Depuration algorithm, two new editing approaches are derived. Moreover, this paper proposes to use neural network ensemble to edit the training data for kNN classifiers. Experiments show that such an approach is better than the approaches derived from Depuration, while these approaches are better than or comparable to Depuration.
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References
Aha, D.W.: Lazy learning: special issue editorial. Artificial Intelligence Review 11, 7–10 (1997)
Barandela, R., Gasca, E.: Decontamination of training samples for supervised pattern recognition methods. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 621–630. Springer, Heidelberg (2000)
Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Dasarathy, B.V.: Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1991)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs (1982)
Ferri, F.J., Albert, J.V., Vidal, E.: Considerations about sample-size sensitivity of a family of edited nearest-neighbor rules. IEEE Transactions on Systems, Man, and Cybernetics - Part B 29, 667–672 (1999)
Koplowitz, J., Brown, T.A.: On the relation of performance to editing in nearest neighbor rules. Pattern Recognition 13, 251–255 (1981)
Sánchez, J.S., Barandela, R., Marqués, A.I., Alejo, R., Badenas, J.: Analysis of new techniques to obtain quality training sets. Pattern Recognition Letters 24, 1015–1022 (2003)
Zhou, Z.-H., Jiang, Y.: Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine 7, 37–42 (2003)
Zhou, Z.-H., Jiang, Y.: NeC4.5: neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering 16 (2004)
Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artificial Intelligence 137, 239–263 (2002)
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Jiang, Y., Zhou, ZH. (2004). Editing Training Data for kNN Classifiers with Neural Network Ensemble. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_60
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DOI: https://doi.org/10.1007/978-3-540-28647-9_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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