Incremental k most similar neighbor classifier for mixed data

Authors

  • Guillermo Sánchez-Díaz Universidad Politécnica de Tulancingo
  • Uriel E. Escobar-Franco Universidad Politécnica de Tulancingo
  • Luis R. Morales-Manilla Universidad Politécnica de Tulancingo
  • Iván Piza-Dávila Instituto Tecnológico y de Estudios Superiores de Occidente https://orcid.org/0000-0002-4189-6208
  • Carlos Aguirre-Salado Universidad Autónoma de San Luis Potosí https://orcid.org/0000-0003-3422-7193
  • Anilu Franco-Arcega Universidad Autónoma del Estado de Hidalgo

Keywords:

Pattern recognition, supervised classification, incremental algorithms, artificial intelligence

Abstract


This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until step t, traversing only once the training data set. Several experiments with synthetic and real data are presented.

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Published

2013-08-16

How to Cite

Sánchez-Díaz, G., Escobar-Franco, U. E., Morales-Manilla, L. R., Piza-Dávila, I., Aguirre-Salado, C., & Franco-Arcega, A. (2013). Incremental k most similar neighbor classifier for mixed data. Revista Facultad De Ingeniería Universidad De Antioquia, (67), 19–30. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/16307