Bayesian Classifier Applied to Higher Education Dropout

被引：3

作者：

Viloria, Amelec ^{[1
]}

Pineda Lezama, Omar Bonerge ^{[2
]}

Varela, Noel ^{[1
]}

机构：

[1] Univ Costa, Cl 58 55-66, Barranquilla 080001, Colombia

[2] Univ Tecnol Cent Amer UNITEC, San Pedro Sula 21101, Honduras

来源：

10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS | 2019年 / 160卷

关键词：

Bayesian networks; Bayesian classifier; educational analysis;

D O I：

10.1016/j.procs.2019.11.045

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-2018 on the basis of socioeconomic data. The Weka tool is then used to perform the classification and the proposed model is statistically compared with other Bayesian classifiers. (C) 2019 The Authors. Published by Elsevier B.V.

引用

页码：573 / 577

页数：5

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