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Novelty Discovery with Kernel Minimum Enclosing Balls

: Sifa, Rafet; Bauckhage, Christian


Kotsireas, I.S.:
Learning and intelligent optimization. 14th International Conference, LION 2020 : Athens, Greece, May 24-28, 2020, Revised Selected Papers
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12096)
ISBN: 978-3-030-53551-3
ISBN: 978-3-030-53552-0
International Conference on Learning and Intelligent Optimization (LION) <14, 2020, Athens/cancelled>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01-S18038A; ML2R
Conference Paper
Fraunhofer IAIS ()

We introduce the idea of utilizing ensembles of Kernel Minimum Enclosing Balls to detect novel datapoints. To this end, we propose a novelty scoring methodology that is based on combining outcomes of the corresponding characteristic functions of a set of fitted balls. We empirically evaluate our model by presenting experiments on synthetic as well as real world datasets.