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2021 | OriginalPaper | Chapter

Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators

Authors : Koji Tabata, Atsuyoshi Nakumura, Tamiki Komatsuzaki

Published in: Trends and Applications in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

A classification bandits problem is a new class of multi-armed bandits problems in which an agent must classify a given set of arms into positive or negative depending on whether the number of bad arms are at least \(N_2\) or at most \(N_1(<N_2)\) by drawing as fewer arms as possible. In our problem setting, bad arms are imperfectly characterized as the arms with above-threshold expected rewards (losses). We develop a method of reducing classification bandits to simpler one threshold classification bandits and propose an algorithm for the problem that classifies a given set of arms correctly with a specified confidence. Our numerical experiments demonstrate effectiveness of our proposed method.

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Footnotes
1
Histopathologists usually diagnose whether cells are of cancer or not by inspecting their morphological characteristics with a human bias, but Raman measurements are considered to enable more reliably to judge the cell states.
 
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Metadata
Title
Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators
Authors
Koji Tabata
Atsuyoshi Nakumura
Tamiki Komatsuzaki
Copyright Year
2021
Book
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-75014-5

Electronic ISBN: 978-3-030-75015-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75015-2_6

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