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ALR n: accelerated higher-order logistic regression

Version 2 2024-06-05, 07:44
Version 1 2020-03-03, 12:23
journal contribution
posted on 2024-06-05, 07:44 authored by Nayyar ZaidiNayyar Zaidi, GI Webb, MJ Carman, F Petitjean, J Cerquides
This paper introduces Accelerated Logistic Regression: a hybrid generative-discriminative approach to training Logistic Regression with high-order features. We present two main results: (1) that our combined generative-discriminative approach significantly improves the efficiency of Logistic Regression and (2) that incorporating higher order features (i.e. features that are the Cartesian products of the original features) reduces the bias of Logistic Regression, which in turn significantly reduces its error on large datasets. We assess the efficacy of Accelerated Logistic Regression by conducting an extensive set of experiments on 75 standard datasets. We demonstrate its competitiveness, particularly on large datasets, by comparing against state-of-the-art classifiers including Random Forest and Averaged n-Dependence Estimators.

History

Journal

Machine Learning

Volume

104

Pagination

151-194

Location

Riva del Garda, ITALY

Open access

  • Yes

ISSN

0885-6125

eISSN

1573-0565

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

2-3

Publisher

SPRINGER