SNDProb: A Probabilistic Approach for Streaming Novelty Detection

Ander Carreño, Iñaki Inza, José Antonio Lozano

Abstract: A probabilistic framework for streaming novelty detection is proposed and illustrated with a mixture of Gaussian distributions that models the set of classes. Instances are predicted based on the probability of belonging to each of the classes. Those for which the model cannot provide confident predictions are introduced into a fixed-sized buffer. When the buffer is full, an Expectation Maximization (EM) algorithm is run to search for new emerging classes in the buffer, and update the current model. The EM algorithm has to deal with an scenario where both probability distributions and instances are available. To overcome this issue, the probability distributions (classes) are weighted. The weights are inferred using a meta-regression model which has been pretrained and supplied with the proposed algorithm. Experiments have been run using synthetic datasets to have a close control over the class arrival strategies, the shape, and the overlapping degree between classes. It is shown that when the assumptions of the probabilistic model are fulfilled, the proposed method outperforms literature non-parametric approaches. Furthermore it obtains competitive results in the case of non-Gaussian classes. The experiments reveal, for the first time, the high sensitivity of the novelty detection algorithms to the class arrival strategies.