Decomposable Naive Bayes Classifier for Partitioned Data


  • Ahmed M. Khedr Computer Science Department, Faculty of Sciences, Sharjah University, Sharjah


Agents, decomposable algorithms, naive Bayes classifier, vertical and horizontal partitions


Most learning algorithms are designed to work on a single dataset. However, with the growth of networks, data is increasingly distributed over many databases in many different geographical sites. These databases cannot be moved to other network sites due to security, size, privacy, or data ownership consideration. In this paper, we propose two decomposable versions of Naive Bayes Classifier for horizontally and vertically partitioned data. The goal of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging minimum local summaries among the participating sites.


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How to Cite

Khedr, A. M. (2013). Decomposable Naive Bayes Classifier for Partitioned Data. COMPUTING AND INFORMATICS, 31(6+), 1511–1531. Retrieved from