Latent Gaussian Model Boosting

Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spati...

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Vydáno v:IEEE transactions on pattern analysis and machine intelligence Ročník 45; číslo 2; s. 1894 - 1905
Hlavní autor: Sigrist, Fabio
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Analytical models
Boosting
Data models
Gaussian process
gaussian processes
Machine learning
mixed effects models
Predictive models
Spatial data
Spatial databases
Splines (mathematics)
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
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Shrnutí:Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spatial data, and it can have difficulty with high-cardinality categorical variables. Latent Gaussian models, such as Gaussian process and grouped random effects models, are flexible prior models which explicitly model dependence among samples and which allow for efficient learning of predictor functions and for making probabilistic predictions. However, existing latent Gaussian models usually assume either a zero or a linear prior mean function which can be an unrealistic assumption. This article introduces a novel approach that combines boosting and latent Gaussian models to remedy the above-mentioned drawbacks and to leverage the advantages of both techniques. We obtain increased prediction accuracy compared to existing approaches in both simulated and real-world data experiments.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2022.3168152