BAYES-NEAREST

BAYES-NEAREST: a new hybrid classifier combining Bayesian network and distance based algorithms. This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which some variables are continuous valued, automatic discretizations of the data are performed. We show the performance of the new proposed approach compared with the Bayesian Network paradigm and with the well known Naive Bayes classifier in some standard databases; the results obtained by the new algorithm are better or equal according to the Wilcoxon statistical test.


References in zbMATH (referenced in 4 articles )

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  1. Kotsiantis, S. B.: Supervised machine learning: a review of classification techniques (2007)
  2. Kotsiantis, S. B.; Zaharakis, I. D.; Pintelas, P. E.: Machine learning: a review of classification and combining techniques (2007) ioport
  3. Lazkano, E.; Sierra, B.; Astigarraga, A.; Martínez-Otzeta, J. M.: On the use of Bayesian Networks to develop behaviours for mobile robots (2007) ioport
  4. Lazkano, Elena; Sierra, Basilio: BAYES-NEAREST: a new hybrid classifier combining Bayesian network and distance based algorithms. (2003)