MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier

Sikha Bagui, Keerthi Devulapalli and Sharon John
Additional contact information
Sikha Bagui: The University of West Florida, USA
Keerthi Devulapalli: University of West Florida, USA
Sharon John: University of West Florida, USA

International Journal of Intelligent Information Technologies (IJIIT), 2020, vol. 16, issue 2, 1-23

Abstract: This study presents an efficient way to deal with discrete as well as continuous values in Big Data in a parallel Naïve Bayes implementation on Hadoop's MapReduce environment. Two approaches were taken: (i) discretizing continuous values using a binning method; and (ii) using a multinomial distribution for probability estimation of discrete values and a Gaussian distribution for probability estimation of continuous values. The models were analyzed and compared for performance with respect to run time and classification accuracy for varying data sizes, data block sizes, and map memory sizes.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJIIT.2020040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:16:y:2020:i:2:p:1-23

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().