Active learning for level set estimation under cost-dependent input uncertainty
As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible. When multiple inspection apparatuses with different costs and precision exist, it is desirable that testing can be carried out cost-effectively by properly controlling the trade-off between the costs and the precision. In this paper, we formulate this as a level set estimation (LSE) problem under cost-dependent input uncertainty - LSE being a type of active learning for estimating the level set, i.e., the subset of the input space in which an unknown function value is greater or smaller than a pre-determined threshold. Then, we propose a new algorithm for LSE under cost-dependent input uncertainty with theoretical convergence guarantee. We demonstrate the effectiveness of the proposed algorithm by applying it to synthetic and real datasets.
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Yu Inatsu (add twitter)
Masayuki Karasuyama (add twitter)
Keiichi Inoue (add twitter)
Ichiro Takeuchi (add twitter)
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09/15/19 06:04PM
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arxiv_cs_LG: Active learning for level set estimation under cost-dependent input uncertainty. Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, and Ichiro Takeuchi https://t.co/I0kCWBMSoO
StatsPapers: Active learning for level set estimation under cost-dependent input uncertainty. https://t.co/61iutWpzbl
arxivml: "Active learning for level set estimation under cost-dependent input uncertainty", Yu Inatsu, Masayuki Karasuyama, … https://t.co/qzWvoHuAVu
arxiv_cs_LG: Active learning for level set estimation under cost-dependent input uncertainty. Yu Inatsu, Masayuki Karasuyama, Keiichi Inoue, and Ichiro Takeuchi https://t.co/I0kCWBMSoO
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