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.
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Samsudin, N.A., Bradley, A.P. (2014). Extended Naïve Bayes for Group Based Classification. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_47
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DOI: https://doi.org/10.1007/978-3-319-07692-8_47
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07691-1
Online ISBN: 978-3-319-07692-8
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