Classification by sparse generalized additive models

Felix Abramovich*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 nearlyminimax (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.

Original languageEnglish
Pages (from-to)2021-2041
Number of pages21
JournalElectronic Journal of Statistics
Volume18
Issue number1
DOIs
StatePublished - 2024

Funding

FundersFunder number
Israel Science FoundationISF1095/22

    Keywords

    • Logistic regression
    • minimaxity
    • misclassification excess risk
    • nonparametric classification
    • sparse group Lasso/Slope

    Fingerprint

    Dive into the research topics of 'Classification by sparse generalized additive models'. Together they form a unique fingerprint.

    Cite this