Abstract
The nearest neighbor classification is a simple and effective technique for pattern recognition. The performance of this technique is known to be sensitive to the distance function used in classifying a test instance. In this paper, we propose a technique to learn sample weights via maximizing classification consistency. Experimental analysis shows that the distance trained in this way enlarges the classification consistency on several datasets and has a strong ability to tolerate noise. Moreover, the proposed approach has better performance than nearest neighbor classification and several state-of-the-art methods.
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Zhu, P., Hu, Q., Yang, Y. (2010). Weighted Nearest Neighbor Classification via Maximizing Classification Consistency. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_37
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DOI: https://doi.org/10.1007/978-3-642-13529-3_37
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