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Improving Bayesian Algorithms for Big Data Classification Based on Map Reduce Framework | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 دی 1403 اصل مقاله (293.64 K) | ||
| نوع مقاله: original | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2025.82037.1054 | ||
| نویسندگان | ||
| Rashin Nimaei* 1؛ Farzad Eskandari2 | ||
| 1Department of Statistics, Allameh Tabatabai University | ||
| 2Allameh Tabataba'i University, Faculty of Statistics, Mathematics and Computer Sciences | ||
| چکیده | ||
| 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 MapReduce In 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. | ||
| کلیدواژهها | ||
| Classification؛ Map-Reduce؛ Correlative Naive Bayes؛ Feature weighting | ||
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آمار تعداد مشاهده مقاله: 12 تعداد دریافت فایل اصل مقاله: 28 |
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