Active learning for online nonlinear Neyman-Pearson classification [Çevrimiçi doǧrusal olmayan Neyman-Pearson sınıflandırması için aktif öǧrenme]

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Can, Başarbatu and Özkan, Hüseyin (2022) Active learning for online nonlinear Neyman-Pearson classification [Çevrimiçi doǧrusal olmayan Neyman-Pearson sınıflandırması için aktif öǧrenme]. In: 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey

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Abstract

Neyman-Pearson (NP) classification framework is suitable for solving binary classification problems with asymmetric error costs such as network intrusion detection and medical diagnosis. In these kind of applications, type I (detecting non-target as target, false positive) and type II (detecting target as non-target, false negative) errors have different consequences. In this paper, we propose an active learning method for online context tree based ensemble NP classifiers. Proposed method prioritizes training samples that have high uncertainty (greater than a constant threshold) among different classifiers of the ensemble model. We report the performance of the proposed active learning method by measuring the moving true positive rates (TPR) and NP scores with respect to the number of samples used in learning. Experiments are carried out on 4 different datasets and proposed model was compared with random sampling method, where new samples are selected randomly from the training set. In addition, we also show that in order to satisfy target false alarm rate of the NP problem, we need to sample training set with and exploration probability, independent from uncertainty measurement.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Active learning; context tree; Neyman-Pearson; online learning
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Hüseyin Özkan
Date Deposited: 25 Mar 2023 15:16
Last Modified: 25 Mar 2023 15:16
URI: https://research.sabanciuniv.edu/id/eprint/45112

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