Parallel k-Most Similar Neighbor Classifier for Mixed Data
Franco Arcega, Anilú
2012
Guillermo Sanchez-Diaz, Anilu Franco-Arcega, Carlos Aguirre-Salado, Ivan Piza-Davila, Luis R. Morales-Manilla, Uriel Escobar-Franco. Parallel k-Most Similar Neighbor Classifier for Mixed Data. In Proc. of the 13th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL). Lecture Notes in Computer Sciences 7435, ISSN: 0302-9743, pp. 586-593, 2012
Abstract
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
