RT journal article
T1 Constrained Naive Bayes with application to unbalanced data classification
A1 Blanquero, Rafael
A1 Carrizosa, Emilio
A1 Ramírez Cobo, Josefa
A1 Sillero-Denamiel, M. Remedios
A2 Estadística e Investigación Operativa
K1 Probabilistic classification
K1 Constrained optimization
K1 Parameter estimation
K1 Efficiency measures
K1 Naïve Bayes
AB The Naive Bayes is a tractable and efficient approach for statistical classification. In general classification problems, the consequences of misclassifications may be rather different in different classes, making it crucial to control misclassification rates in the most critical and, in many realworld problems, minority cases, possibly at the expense of higher misclassification rates in less problematic classes. One traditional approach to address this problem consists of assigning misclassification costs to the different classes and applying the Bayes rule, by optimizing a loss function. However, fixing precise values for such misclassification costs may be problematic in realworld applications. In this paper we address the issue of misclassification for the Naive Bayes classifier. Instead of requesting precise values of misclassification costs, threshold values are used for different performance measures. This is done by adding constraints to the optimization problem underlying the estimation process. Our findings show that, under a reasonable computational cost, indeed, the performance measures under consideration achieve the desired levels yielding a user-friendly constrained classification procedure.
PB SPRINGER
SN 1435-246X
YR 2021
FD 2021-10
LK http://hdl.handle.net/10498/27270
UL http://hdl.handle.net/10498/27270
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026