Bayesian active learning with abstention feedbacks
Autor: | Huan Xu, Cuong V. Nguyen, Lam Si Tung Ho, Vu Dinh, Binh T. Nguyen |
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Rok vydání: | 2022 |
Předmět: |
Mathematical optimization
Active learning (machine learning) Computer science Cognitive Neuroscience Bayesian probability 020207 software engineering 02 engineering and technology Function (mathematics) Computer Science Applications Constant factor Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Greedy algorithm Value (mathematics) |
Zdroj: | Neurocomputing. 471:242-250 |
ISSN: | 0925-2312 |
Popis: | We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a ( 1 - 1 e ) constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios. |
Databáze: | OpenAIRE |
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