Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection
Josef Kittler, Cemre Zor, Ioannis Kaloskampis, Yulia Hicks, Wenwu Wang · Pattern Recognition · 2017
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.