MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier

被引：6
|
作者：
Bagui, Sikha [1 ]
Devulapalli, Keerthi [2 ]
John, Sharon [2 ]
机构：
[1] Univ West Florida, Dept Comp Sci, Pensacola, FL 32514 USA
[2] Univ West Florida, Pensacola, FL USA
来源：
INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES | 2020年 / 16卷 / 02期
关键词：
Big Data; Discrete Naive Bayes Model; Hadoop; MapReduce Environment; Mixed Naive Bayes Model; Naive Bayes Classification; Parallel Naive Bayes Implementation; Probability-Based Classification;
D O I：
10.4018/IJIIT.2020040101
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
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页码：1 / 23
页数：23
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