Learning random subspace novelty detection filters

In this paper we propose a novelty detection framework based on the orthogonal projection operators and the bootstrap idea. Our approach called Random Subspace Novelty Detection Filter (RS - NDF) combines the sampling technique and the ensemble idea. RS - NDF is an ensemble of NDF, induced from bootstrap samples of the training data, using random feature selection in the NDF induction process. Prediction is made by aggregating the predictions of the ensemble. RS - NDF generally exhibits a substantial performance improvement over the single NDF. Thanks to an online learning algorithm, the RS-NDF approach is also able to track changes in data over time. The RS - NDF method is compared to single NDF and other novelty detection methods with tenfold cross-validation experiments on publicly available datasets, where the methods superiority is demonstrated. Performance metrics such as precision and recall, false positive rate and false negative rate, F-measure, AUC and G-mean are computed. The proposed approach is shown to improve the prediction accuracy of the novelty detection, and have favorable performance compared to the existing algorithms.