Neural-ANOVA: Analytical Model Decomposition using Automatic Integration

Fuente: arXiv
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Main Authors: Limmer, Steffen, Udluft, Steffen, Otte, Clemens
Format: Preprint
Published: 2024
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author Limmer, Steffen
Udluft, Steffen
Otte, Clemens
author_facet Limmer, Steffen
Udluft, Steffen
Otte, Clemens
contents The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into the sum of lower-order models using the functional ANOVA decomposition. Our approach formulates a learning problem, which enables fast analytical evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to provide insights into the approximation properties compared to other regression approaches from the literature.
format Preprint
id arxiv_https___arxiv_org_abs_2408_12319
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Neural-ANOVA: Analytical Model Decomposition using Automatic Integration
Limmer, Steffen
Udluft, Steffen
Otte, Clemens
Machine Learning
The analysis of variance (ANOVA) decomposition offers a systematic method to understand the interaction effects that contribute to a specific decision output. In this paper we introduce Neural-ANOVA, an approach to decompose neural networks into the sum of lower-order models using the functional ANOVA decomposition. Our approach formulates a learning problem, which enables fast analytical evaluation of integrals over subspaces that appear in the calculation of the ANOVA decomposition. Finally, we conduct numerical experiments to provide insights into the approximation properties compared to other regression approaches from the literature.
title Neural-ANOVA: Analytical Model Decomposition using Automatic Integration
topic Machine Learning
url https://arxiv.org/abs/2408.12319