mlsanityData to 7 Oct 2026

Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions

arXiv 2209.15409 ↗First posted 30 Sep 2022cs.LGcited by 7

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Abstract (arXiv)

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an interpretable machine learning model that effectively and efficiently captures feature interactions of arbitrary orders. HONAMs improve predictive accuracy without compromising interpretability, an essential requirement in high-stakes applications. This advantage of HONAM can help analyze and extract high-order interactions present in datasets. The source code for HONAM is publicly available at https://github.com/gim4855744/HONAM/.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 38 of its 64 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

38 of 64 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2025Additive Models Explained: A Computational Complexity Approachcited by 3
  2. 2024Transparent Networks for Multivariate Time Seriescited by 2
  3. 2025Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networkscited by 2
  4. 2024Neural Additive Image Model: Interpretation through Interpolationcited by 1
  5. 2025RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classificationcited by 1
  6. 2025Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictabilitycited by 0
  7. 2026EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Modelscited by 0