A Nearest Neighbor Open-Set Classifier based on Excesses of Distance Ratios. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Steyn, Matthys Lucas; de Wet, Tertius; De Baets, Bernard; Luca, Stijn
• Is Part Of: Journal of computational and graphical statistics. Volume 32:Issue 1(2023)
• Page Start: 319
• Page End: 328
• Publication Date: 2023-01-02
• Subjects: Classification -- Extreme value theory -- Generalized Pareto distribution -- Open-set recognition; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2022.2096621
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: This article proposes an open-set recognition model that is based on the use of extreme value statistics. For this purpose, a distance ratio is introduced that expresses how dissimilar a target point is from the known classes by considering the ratio of distances locally around the target point. It is shown that the class of generalized Pareto distributions with bounded support can be used to model the peaks of the distance ratio above a high threshold. The resulting distribution provides a probabilistic framework to perform open-set recognition. Furthermore, we describe a numerical procedure to estimate the hyperparameters of our model. This procedure is based on a new objective function that considers both the fit of the generalized Pareto distribution and the misclassification error of the known classes. Our method is applied to three image datasets and an audio dataset showing that it outperforms similar open-set recognition and anomaly detection methods. Supplementary materials for this article are available online.