Abstract
In this paper, we consider a multi-class classifier for problems where unknown classes are included in testing phase. Previous classifiers consider the “closed-set” case where the classes used for training and the classes used for testing are the same. A more realistic case is the “open-set” recognition, in which only a limited number of classes appear at training time, and unknown classes appear during testing. To handle such problems, we need classifiers that accurately classify data belonging to not only known classes but also unknown classes. In this paper, We introduce a Support Vector Machine (SVM) based Extreme Value Machine (EVM) to determine a compact class region. Any data outside of such class regions is rejected as being in unknown classes. To construct a class region, we approach the class decision boundary found by SVM towards the samples, by removing some support vectors close to the boundary. This SVM based EVM resolves the three problems that EVM possesses: unfair size of class regions, excessive sensibility to certain points and fragmentation of a class region.
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Acknowledgment
This work was partially supported by JSPS KAKENHI Grant Number 19H04128.
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Kaneko, Y., Kudo, M. (2021). SVM Based EVM for Open Space Problems. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_24
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DOI: https://doi.org/10.1007/978-3-030-89691-1_24
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