PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION
Description
PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY
SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION
GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN, R. KURT UNGAR
ABSTRACT. One-class Bayes learning such as one-class Naïve Bayes and one-class Bayesian
Network employs Bayes learning to build a classifier on the positive class only for discriminating
the positive class and the negative class. It has been applied to anomaly detection for identifying
abnormal behaviors that deviate from normal behaviors. Because one-class Bayes classifiers can
produce probability score, which can be used for defining anomaly score for anomaly detection,
they are preferable in many practical applications as compared with other one-class learning
techniques. However, previously proposed one-class Bayes classifiers might suffer from poor
probability estimation when the negative training examples are unavailable. In this paper, we
propose a new method to improve the probability estimation. The improved one-class Bayes
classifiers can exhibits high performance as compared with previously proposed one-class Bayes
classifiers according to our empirical results.
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PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION |
https://c3.nasa.gov/dashlink/static/media/publication/Paper_13_.pdf |