TY - GEN
T1 - Naive Bayesian classifiers with extreme probability features
AU - van der Gaag, L.C.
AU - Capotorti, A.
PY - 2018
Y1 - 2018
N2 - Despite their popularity, naive Bayesian classifiers are not well suited for real-world applications involving extreme probability features. As will be demonstrated in this paper, methods used to forestall the inclusion of zero probability parameters in naive classifiers have quite counterintuitive effects. An elegant, principled solution for handling extreme probability events is available however, from coherent conditional probability theory. We will show how this theory can be integrated in standard naive Bayesian classifiers, and then present a computational framework that retains the classifiers’ efficiency in the presence of a limited number of extreme probability features.
AB - Despite their popularity, naive Bayesian classifiers are not well suited for real-world applications involving extreme probability features. As will be demonstrated in this paper, methods used to forestall the inclusion of zero probability parameters in naive classifiers have quite counterintuitive effects. An elegant, principled solution for handling extreme probability events is available however, from coherent conditional probability theory. We will show how this theory can be integrated in standard naive Bayesian classifiers, and then present a computational framework that retains the classifiers’ efficiency in the presence of a limited number of extreme probability features.
KW - Naive Bayesian classifiers
KW - Extreme probabilities
KW - Coherent conditional probabilitytheory
KW - Computational efficiency
M3 - Conference contribution
VL - 72
T3 - Proceedings of Machine Learning Research
SP - 499
EP - 510
BT - Proceedings of Machine Learning Research
ER -