Bayesian And Naive Bayesian Decision Boundaries For Multidimensional Cases
Dhiadeen Mohammed Salih, Maher Faiq Esmail · 2019
Naive Bayesian classifier is a fundamental statistical method that assents the conditional independence of attribute values and enumerates optimal classifier by minimizing the probability errors within the classes. In practice, Naive Bayesian classifier often violated assumptions and is not robust to the noise with multidimensional cases. In this work, the Bayesian and Naive Bayesian decision regions and discriminant functions are discussed for a multiclass, multi features problem. The action all of covariance, variance and correlation possibilities are addressed with examples. The results give good understanding of the distance between data spars and decision surface among decision regions. This may be more convenient to show the classifier behavior when the involved probability distribution functions (pdf) are complicated.