Published June 28, 2022
| Version v1
Publication
Constrained Naïve Bayes with application to unbalanced data classification
Description
The Naïve 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 appli cations. In this paper we address the issue of misclassification for the Naïve Bayes
classifier. Instead of requesting precise values of misclassification costs, threshold val ues 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 con sideration achieve the desired levels yielding a user-friendly constrained classification
procedure.
Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/134739
- URN
- urn:oai:idus.us.es:11441/134739