Abstract
In order to classify an unseen (query) vector q with the k-Nearest Neighbors method (k-NN) one computes a similarity function between q and training vectors in a database. In the basic variant of the k-NN algorithm the predicted class of q is estimated by taking the majority class of the q’s k-nearest neighbors. Various similarity functions may be applied leading to different classification results. In this paper a heterogeneous similarity function is constructed out of different 1-component metrics by minimization of the number of classification errors the system makes on a training set. The HSFL-NN system, which has been introduced in this paper, on five tested datasets has given better results on unseen samples than the plain k-NN method with the optimally selected k parameter and the optimal homogeneous similarity function.
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Grudziński, K. (2008). Towards Heterogeneous Similarity Function Learning for the k-Nearest Neighbors Classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_56
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DOI: https://doi.org/10.1007/978-3-540-69731-2_56
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