Decision Boundaries in the sense of Naive Bayesian for Multidimensional cases (Naïve Decision Surface Network)

Naive Bayesian classifier is a fundamental statistical method that assents the conditional independence of features values 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. A useful way to signify classifier is through discriminant functions where the classifier assigns a feature vector to divide the feature space into decision surfaces separated by multidimensional boundaries. In this work, Naïve Decision Surface Network is proposed to build on discriminant quadratic functions that obtained for a multiclass, multi features problems. The action all of covariance, variance and correlation possibilities are addressed. An example is illustrated to demonstrate the computational and analytical simplifications and the results showed less classification rate error.