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Journal Articles Journal of Applied Statistics Year : 2022

Streaming constrained binary logistic regression with online standardized data

Abstract

Online learning is a method for analyzing very large datasets ("big data") as well as data streams. In this article, we consider the case of constrained binary logistic regression and show the interest of using processes with an online standardization of the data, in particular to avoid numerical explosions or to allow the use of shrinkage methods. We prove the almost sure convergence of such a process and propose using a piecewise constant step-size such that the latter does not decrease too quickly and does not reduce the speed of convergence. We compare twenty-four stochastic approximation processes with raw or online standardized data on five real or simulated data sets. Results show that, unlike processes with raw data, processes with online standardized data can prevent numerical explosions and yield the best results.
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Dates and versions

hal-02156324 , version 1 (14-06-2019)
hal-02156324 , version 2 (10-07-2020)
hal-02156324 , version 3 (04-12-2020)
hal-02156324 , version 4 (07-01-2021)

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Cite

Benoît Lalloué, Jean-Marie Monnez, Eliane Albuisson. Streaming constrained binary logistic regression with online standardized data. Journal of Applied Statistics, 2022, 49 (6), pp.1519-1539. ⟨10.1080/02664763.2020.1870672⟩. ⟨hal-02156324v4⟩

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