Online kernel-based nonlinear Neyman-Pearson classification

Can, Başarbatu and Kerpiççi, Mine and Özkan, Hüseyin (2021) Online kernel-based nonlinear Neyman-Pearson classification. In: 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands

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We propose a novel Neyman-Pearson (NP) classification algorithm, which achieves the maximum detection rate and meanwhile keeps the false alarm rate around a user-specified threshold. The proposed method processes data in an online framework with nonlinear modeling capabilities by transforming the observations into a high dimensional space via the random Fourier features. After this transformation, we use a linear classifier whose parameters are sequentially learned. We emphasize that our algorithm is the first online Neyman-Pearson classifier in the literature, which is suitable for both linearly and nonlinearly separable datasets. In our experiments, we investigate the performance of our algorithm on well-known datasets and observe that the proposed online algorithm successfully learns the nonlinear class separations (by outperforming the linear models) while matching the desired false alarm rate.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Neyman-Pearson, online learning, nonlinear classification, kernel, random projections, Fourier features, perceptron.
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Hüseyin Özkan
Date Deposited: 27 Aug 2021 16:46
Last Modified: 02 Sep 2022 12:30

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