Abstract
The k-NN algorithm is still very popular due to its simplicity and the easy interpretability of the results. However, the often used Euclidean distance is an arbitrary choice for many datasets. It is arbitrary because often the data is described by measurements from different domains. Therefore, the Euclidean distance often leads to a bad classification rate of k-NN. By feature weighting the scaling of dimensions can be adapted and the classification performance can be significantly improved. We here present a simple linear programming based method for feature weighting, which in contrast to other feature weighting methods is robust to the initial scaling of the data dimensions. An evaluation is performed on real-world datasets from the UCI repository with comparison to other feature weighting algorithms and to Large Margin Nearest Neighbor Classification (LMNN) as a metric learning algorithm.
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References
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Gilad-Bachrach, R., Navot, A., Tishby, N.: Margin based feature selection - theory and algorithms. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, pp. 43–50. ACM, New York (2004)
Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Netw. 15(8-9), 1059–1068 (2002)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proc. 9th International Workshop on Machine Learning, pp. 249–256 (1992)
Li, Y., Lu, B.L.: Feature selection based on loss-margin of nearest neighbor classification. Pattern Recogn. 42(9), 1914–1921 (2009)
Sun, Y., Li, J.: Iterative relief for feature weighting. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 913–920. ACM, New York (2006)
Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems 19. MIT Press, Cambridge (2006)
Weinberger, K.Q., Saul, L.K.: Fast solvers and efficient implementations for distance metric learning. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1160–1167. ACM, New York (2008)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)
Weinberger, K., Sha, F., Saul, L.: Convex optimizations for distance metric learning and pattern classification. Signal Processing Magazine 27, 146–158 (2010)
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Hocke, J., Martinetz, T. (2013). Feature Weighting by Maximum Distance Minimization. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_53
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DOI: https://doi.org/10.1007/978-3-642-40728-4_53
Publisher Name: Springer, Berlin, Heidelberg
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