Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection

Reza Mohammadi-Ghazi, Youssef M. Marzouk, Oral Büyüköztürk

Abstract: Highlights•A new novelty detection classifier is proposed.•The proposed method is capable of considering the dependencies of relevant random variables.•No simplifying assumption is made for encoding such dependencies.•Combining additive modeling and boosting method to learn conditional densities.•12%–24% false positive reduction compared to one-class SVM and two other methods.