Extended Naïve Bayes for Group Based Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 287)

Abstract

This paper focuses on extending Naive Bayes classifier to address group based classification problem. The group based classification problem requires labeling a group of multiple instances given the prior knowledge that all the instances of the group belong to same unknown class. We present three techniques to extend the Naïve Bayes classifier to label a group of homogenous instances. We then evaluate the extended Naïve Bayes classifier on both synthetic and real data sets and demonstrate that the extended classifiers may be a promising approach in applications where the test data can be arranged into homogenous subsets.

Keywords

group based classification Naïve Bayes classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandQLDAustralia

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