NDLI: Multi-segments Naïve Bayes classifier in likelihood space

Content Provider	IET Digital Library
Author	Zhao, Zhenchong Wang, Xiaodan
Abstract	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.
Starting Page	882
Ending Page	891
Page Count	10
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 6, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0546
Journal	IET Computer Vision
Publisher Date	2018-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Conditional Probability Distribution Data Handling Technique Discriminant Function Distance-measure Method Error Reduction Incorrect Estimation Model Likelihood Space Mapped Data Multisegments Naïve Bayes Classifier Nonlinear Projection Method Pattern Classification Posterior Probability Statistical Distribution Statistics Training Process
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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