Study of Locally Adaptive Naive Bayes Learner

The title: Locally Adaptive Naïve Bayes Framework Design via Density Based Clustering

The codes written in MATLAB, the experimental outputs in Excel, the suit of test dataset, and other relevant documents can be publicly for examinations and progressive studies. Please clique the related files below to download.

For any question related with this study, please e-mail farukbulut(at)esenyurt(dot)edu(dot)tr

Best wishes. 

Dr. Faruk BULUT

The web link of the book chapter is here.

ABSTRACT:

In this chapter, local conditional probabilities of a query point are used in classification rather than consulting a generalized framework containing a conditional probability. In the proposed locally adaptive naïve Bayes (LANB) learning style, a certain amount of local instances, which are close the test point, construct an adaptive probability estimation. In the empirical studies of over the 53 benchmark UCI datasets, more accurate classification performance has been obtained. A total 8.2% increase in classification accuracy has been gained with LANB when compared to the conventional naïve Bayes model. The presented LANB method has outperformed according to the statistical paired t-test comparisons: 31 wins, 14 ties, and 8 losses of all UCI sets.