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Erschienen in:
FRLS: A Forecasting Model with Robust and Reduced Redundancy Latent Series Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

DEAL: Data-Efficient Active Learning for Regression Under Drift

verfasst von : Béla H. Böhnke, Edouard Fouché, Klemens Böhm

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Current work on Active Learning (AL) tends to assume that the relationship between input and target variables does not change, i.e., the oracle is static. However, oracles can be stream-like and exhibit concept drift, which requires updating the learned relationship. Standard drift detection and adaption methods rely on constantly observing the target variables, which is too costly in AL. Current work on AL for regression has not addressed the challenge of frequently drifting oracles. We propose a new AL method that estimates its error due to drift by learning statistics about how often and how severe drift occurs, based on a Gaussian Process model with a time-variant kernel. Whenever the estimated error reaches a user-required threshold, our model measures the target variables and recalibrates the learned relationship as well as the drift statistics. Our drift-aware model requires up to 20 times fewer measurements than widely used methods.

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Vorheriges Kapitel DiffFind: Discovering Differential Equations from Time Series
Nächstes Kapitel Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting
Fußnoten
2
Note one can reduce the complexity of DEAL down to \(O(n \cdot i)\) (with learning epochs \(i \ll n\)), by using gradient-based GP models and batch training. Further, one can cap the number of measurements used for training, reducing the factor n to a constant.
 
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Metadaten
Titel
DEAL: Data-Efficient Active Learning for Regression Under Drift
verfasst von
Béla H. Böhnke
Edouard Fouché
Klemens Böhm
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-65-5

Electronic ISBN: 978-981-9722-66-2

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-2266-2_15

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