Multi‐segments Naïve Bayes classifier in likelihood space - Zhenchong Zhao; Xiaodan Wang (IET Computer Vision, 2018)
Naïve Bayes (NB) classifier has shown amazing performance in many real applications. However, the true probability distributions are usually unknown and tend to be quite complicated with high feature dimensions. Incorrect estimation models will decrease classification performance. In this study, a new method named multi‐segment NB classifier is proposed to reduce errors caused by improper estimation models by implementing the classification directly in the likelihood space rather than through calculating posterior probability. The estimation of the conditional probability distribution is treated as a non‐linear projection method which maps the original features into the likelihood space. Then, the mapped data is divided into some successive sub‐segments and the classifier in each segment is trained by the corresponding sub‐dataset, respectively. The discriminant functions are learned through a distance‐measure method instead of a probability‐based way and the parameters of the former classifier are used in the next training process in order to decrease the searching space. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.