Bayesian nonparametric estimation for big data classification

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Nimaei Rashin; Eskandari Farzad

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Journal Paper

Paper Information

Journal: JOURNAL OF DATA SCIENCE AND MODELING Year:2024 | Volume:3 | Issue:2 Page(s): 1-14

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Information Journal Paper

Title

Bayesian nonparametric estimation for big data classification

Author(s)

Nimaei Rashin | Eskandari Farzad | Issue Writer Certificate

Keywords

Classification

Map-Reduce

Correlative Naive Bayes

Feature weighting

Abstract

‎The recent advancements in technology have faced an increase in the growth rate of data‎.‎According to the amount of data generated‎, ‎ensuring effective analysis using traditional approaches becomes very complicated‎.‎One of the methods of managing and analyzing big data is classification‎.‎%One of the data mining methods used commonly and effectively to classify big data is the MapReduce‎‎In this paper‎, ‎the feature weighting technique to improve Bayesian classification algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model‎.‎%Classification models include Naive Bayes classifier‎, ‎correlated Naive Bayes and correlated Naive Bayes with feature weighting‎.‎Correlated Naive Bayes classification is a generalization of the Naive Bayes classification model by considering the dependence between features‎.‎%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes classification‎.‎The performance of all described methods are evaluated by considering accuracy‎, ‎sensitivity and specificity‎, ‎accuracy‎, ‎sensitivity and specificity metrics.

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