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Issue No.04 - April (2010 vol.32)
pp: 763-765
Andrea Argentini , Università di Trento, Italy
Enrico Blanzieri , Università di Trento, Italy
In a 2006 TPAMI paper, Wang proposed the Neighborhood Counting Measure [2], a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the Minimum Risk Metric (MRM, [1]), an early distance measure based on the minimization of the risk of misclassification. Wang did not compare NCM to MRM because of its allegedly excessive computational load. In this comment paper, we complete the comparison that was missing in Wang's paper and, from our empirical evaluation, we show that MRM outperforms NCM and that its running time is not prohibitive as Wang suggested.
Pattern recognition, machine learning, k-Nearest Neighbors, distance measures, MRM, NCM.
Andrea Argentini, Enrico Blanzieri, "About Neighborhood Counting Measure Metric and Minimum Risk Metric", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 4, pp. 763-765, April 2010, doi:10.1109/TPAMI.2009.69
[1] E. Blanzieri and F. Ricci, "A Minimum Risk Metric for Nearest Neighbor Classification," Proc. 16th Int'l Conf. Machine Learning, pp. 22-31, 1999.
[2] H. Wang, "Nearest Neighbors by Neighborhood Counting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 942-953, June 2006, http://doi. TPAMI.2006.126
[3] S. Mahamud and M. Hebert, "Minimum Risk Distance Measure for Object Recognition," Proc. Int'l Conf. Computer Vision, pp. 242-248, 2003, iccv/2003/1950/01195010242.
[4] H. Wang, "Personal communication," 2007.
[5] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
[6] A. Argentini and E. Blanzieri, "Neighborhood Counting Measure Metric and Minimum Risk Metric: An Empirical Comparison," Technical Report DISI-08-057, DISI, Univ. of Trento Italy, 2008.
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