An empirical study of a simple naive Bayes classifier based on ranking functions

Kinzang Chhogyal*, Abhaya Nayak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

Ranking functions provide an alternative way of modelling uncertainty. Much of the research in this area focuses on its theoretical and philosophical aspects. Approaches to solving practical problems involving uncertainty have been, by and large, dominated by probabilistic models of uncertainty. In this paper we investigate if ranking functions can be used to solve practical problems in an uncertain domain. In particular, we look at the problem of identifying spam e-mails, one of the earliest success stories of probabilistic machine learning techniques. We show how the probabilistic naive Bayes classifier can easily be translated to one based on ranking functions, and present some experimental results that demonstrate its efficacy in correctly identifying spam e-mails.

Original languageEnglish
Title of host publicationAI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages324-331
Number of pages8
Volume9992 LNAI
ISBN (Print)9783319501260
DOIs
Publication statusPublished - 2016
Event29th Australasian Joint Conference on Artificial Intelligence, AI 2016 - Hobart, Australia
Duration: 5 Dec 20168 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9992 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other29th Australasian Joint Conference on Artificial Intelligence, AI 2016
CountryAustralia
CityHobart
Period5/12/168/12/16

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Chhogyal, K., & Nayak, A. (2016). An empirical study of a simple naive Bayes classifier based on ranking functions. In AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings (Vol. 9992 LNAI, pp. 324-331). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9992 LNAI). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-50127-7_27