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.