- Title
- Improving Naive Bayes classifier using conditional probabilities
- Creator
- Taheri, Sona; Mammadov, Musa; Bagirov, Adil
- Date
- 2010
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/31612
- Identifier
- vital:4877
- Identifier
- http://www.scopus.com/inward/record.url?eid=2-s2.0-84870568310&partnerID=40&md5=7096cf62fd30c407ba409f270b915413
- Abstract
- Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness. © 2011, Australian Computer Society, Inc.
- Publisher
- Ballarat, VIC Australian Computer Society, Inc.
- Rights
- Copyright Australian Computer Society, Inc.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian networks; Correlation; Naive Bayes; Semi Naive Bayes; Bayesian network classifiers; Conditional probabilities; Data classification problems; Independence assumption; Naive Bayes classifiers; Numerical experiments; Real world data; Real-world application; Algorithms; Classifiers; Information technology; Learning systems; Optical correlation
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