@article{discovery10067204,
           pages = {30--44},
          volume = {77},
            note = {{\copyright} 2017 The Authors. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)},
            year = {2018},
           title = {Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection},
       publisher = {ELSEVIER SCI LTD},
         journal = {Pattern Recognition},
           month = {May},
        keywords = {Anomaly detection,
Classifier decision incongruence,
Bayesian surprise},
             url = {https://doi.org/10.1016/j.patcog.2017.11.031},
            issn = {1873-5142},
          author = {Kittler, J and Zor, C and Kaloskampis, I and Hicks, Y and Wang, W},
        abstract = {The state of classifier incongruence in decision making systems incorporating multiple classifiers is often
an indicator of anomaly caused by an unexpected observation or an unusual situation. Its assessment is
important as one of the key mechanisms for domain anomaly detection. In this paper, we investigate the
sensitivity of Delta divergence, a novel measure of classifier incongruence, to estimation errors. Statistical properties of Delta divergence are analysed both theoretically and experimentally. The results of the
analysis provide guidelines on the selection of threshold for classifier incongruence detection based on
this measure.}
}