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A Domain Adaptation Approach for Performance Estimation of Spatial Predictions
- Source :
- IEEE Transactions on Geoscience and Remote Sensing. 59:5197-5205
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Spatial predictions, like other supervised learning tasks, require some criterion for a predictor’s quality. Typical data-splitting schemes, such as holdouts and $k$ -fold cross-validation, ignore the fact that the training data are usually not available where predictions are being made. The common data-splitting schemes are thus biased estimates of a predictor’s performance, which in turn may lead to choosing suboptimal predictors. In this contribution, we borrow ideas from the domain adaptation machine-learning literature, to suggest the importance-weighted source risk (IWSR). IWSR is a principled approach for weighting the prediction risk, which allows the practitioner to explicitly state the target locations for prediction. IWSR essentially consists of down-weighting training locations and up-weighting target locations. We show that, unlike the usual (unweighted) empirical risk, IWSR is an unbiased estimator of the prediction error. Equipped with this risk estimator, we use it to learn a model in the empirical risk minimization framework and to evaluate the existing predictors. We show the superiority of this weighted risk, using both simulated data and an empirical control: air-temperature prediction in France.
- Subjects :
- Estimation
business.industry
Computer science
media_common.quotation_subject
Mean squared prediction error
Supervised learning
0211 other engineering and technologies
Estimator
02 engineering and technology
Machine learning
computer.software_genre
Weighting
Bias of an estimator
Task analysis
General Earth and Planetary Sciences
Quality (business)
Artificial intelligence
Empirical risk minimization
Electrical and Electronic Engineering
business
computer
021101 geological & geomatics engineering
media_common
Subjects
Details
- ISSN :
- 15580644 and 01962892
- Volume :
- 59
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Geoscience and Remote Sensing
- Accession number :
- edsair.doi...........6eafb2a837cdf31b73fa7c736187584a