Educational database analysis using simple bayesian classifier

Abstract

In this article, we propose the use of a new simple Bayesian classifier (SBND) that quickly learns a Markov boundary of the class variable to a network structure relating class variables and the said boundary. This model is compared to other Bayesian classifiers. Then experimental tests are carried out using a UCI database referring to the performance of high school students who take the Mathematics course in two schools in Portugal based on their personal details the tests try to anticipate who passes the course and who requires help to succeed. With these databases we compare the results obtained by such algorithms studied in the state of the art such as Naive Bayes, TAN, BAN, RPDag, CRPDag, SBND and combinations with different metrics such as K2, BIC, Akaike, BDEu. The experimental work was done in Weka software.