Abstract
Many real-world applications are perturbed by the misprediction of the unknown instances into the known or seen domain. The issue is more compounded when we have to recognize the unknowns as well as correctly classify the knowns in a mixed bag of known and unknown instances. In this article, we present a scheme that can efficiently classify instances from the seen classes and can also detect instances coming from unseen (unknown) classes. We have integrated the principles of reverse nearest neighborhood and the principles of intuitionistic fuzzy sets for this purpose. Reverse nearest neighborhood provides a natural and elegant way of tackling the issue of unknown class without incommoding the known class classifications. Further, we incorporate intuitionistic fuzzy sets to infer the unknown class memberships of the instances from the reverse nearest neighbor information of the known classes. Empirical evidence on five real-world datasets indicates the improved efficaciousness of the proposed method over six state-of-the-art competing methods.
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Sadhukhan, P., Palit, S. (2024). Be Informed of the Known to Catch the Unknown. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_7
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