Open Access
2024 Classification by sparse generalized additive models
Felix Abramovich
Author Affiliations +
Electron. J. Statist. 18(1): 2021-2041 (2024). DOI: 10.1214/24-EJS2246


We consider (nonparametric) sparse (generalized) additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the coefficients of univariate additive components’ expansions in orthonormal series (e.g., Fourier or wavelets). The resulting classifier is inherently adaptive to the unknown sparsity and smoothness. We show that under certain sparse group restricted eigenvalue condition it is nearly-minimax (up to log-factors) simultaneously across the entire range of analytic, Sobolev and Besov classes. The performance of the proposed classifier is illustrated on a simulated and a real-data examples.

Funding Statement

The work was supported by the Israel Science Foundation (ISF), Grant ISF-1095/22.


The author is grateful to Tomer Levy for valuable remarks.


Download Citation

Felix Abramovich. "Classification by sparse generalized additive models." Electron. J. Statist. 18 (1) 2021 - 2041, 2024.


Received: 1 December 2023; Published: 2024
First available in Project Euclid: 9 May 2024

Digital Object Identifier: 10.1214/24-EJS2246

Primary: 62G05 , 62H30

Keywords: logistic regression , minimaxity , misclassification excess risk , Nonparametric classification , sparse group Lasso/Slope

Vol.18 • No. 1 • 2024
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