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Journal article
MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier
International journal of intelligent information technologies, Vol.16(2)
04/01/2020
Web of Science ID: WOS:000547485900001
Abstract
This study presents an efficient way to deal with discrete as well as continuous values in Big Data in a parallel Naive 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.
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Details
- Title
- MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier
- Publication Details
- International journal of intelligent information technologies, Vol.16(2)
- Resource Type
- Journal article
- Publisher
- IGI Global
- Number of pages
- 23
- Copyright
- © 2020 IGI Global
- Identifiers
- WOS:000547485900001; 99380178996106600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering
- Language
- English