This paper presents a paralellization of the incremental algorithm inc-k-msn, for mixed data and similarity functions that do not satisfy metric properties. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k-most similar neighbors processed in step t, traversing only once the training data set. Several experiments with synthetic and real data are presented. © 2012 Springer-Verlag.
CITATION STYLE
Sanchez-Diaz, G., Franco-Arcega, A., Aguirre-Salado, C., Piza-Davila, I., Morales-Manilla, L. R., & Escobar-Franco, U. (2012). Parallel k-most similar neighbor classifier for mixed data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7435 LNCS, pp. 586–593). https://doi.org/10.1007/978-3-642-32639-4_71
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