Abstract
One of the most popular techniques in pattern recognition applications is the nearest neighbours (K-NN) classification rule based on the Euclidean distance function. This rule can be modified by data transformations. Variety of distance functions can be induced from data sets in this way. We take into considerations inducing distance functions by linear data transformations. The results of our experiments show the possibility of improving K-NN rules through such transformations.
This work was partially supported by the W/II/1/2004 and SPB-M (COST 282) grants from the Białystok University of Technology and by the 16/St/2004 grant from the Institute of Biocybernetics and Biomedical Engineering PAS.
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Bobrowski, L., Topczewska, M. (2004). Improving the K-NN Classification with the Euclidean Distance Through Linear Data Transformations. In: Perner, P. (eds) Advances in Data Mining. ICDM 2004. Lecture Notes in Computer Science(), vol 3275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30185-1_3
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DOI: https://doi.org/10.1007/978-3-540-30185-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24054-9
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