A hybrid APSO-aided learnable bayesian classifier

Satchidanada Dehuri, Bijaya Kumar Nanda, Sung Bae Cho

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

5 Citations (Scopus)

Abstract

In this paper a hybrid adaptive particle swarm optimization aided learnable Bayesian classifier is proposed. The objective of the classifier is to solve some of the fundamental problems associated with the pure naive Bayesian classifier and its variants with a larger view towards maximization of the classifier accuracy. Further, the proposed algorithm can exhibits an improved capability to eliminate spurious features from large datasets aiding researchers in identifying those features that are solely responsible for achieving higher classification accuracy. The effectiveness of the proposed algorithm is demonstrated on several benchmark datasets.

Original languageEnglish
Title of host publicationProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
Pages695-706
Number of pages12
Publication statusPublished - 2009
Event4th Indian International Conference on Artificial Intelligence, IICAI 2009 - Tumkur, India
Duration: 2009 Dec 162009 Dec 18

Publication series

NameProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009

Other

Other4th Indian International Conference on Artificial Intelligence, IICAI 2009
CountryIndia
CityTumkur
Period09/12/1609/12/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Dehuri, S., Nanda, B. K., & Cho, S. B. (2009). A hybrid APSO-aided learnable bayesian classifier. In Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 (pp. 695-706). (Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009).