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ALR n: accelerated higher-order logistic regression

AuthorsZaidi, Nayyar Abbas; Webb, Geoffrey I.; Carman, Mark J.; Petitjean, François; Cerquides, Jesús
KeywordsGenerative-discriminative learning
Low-bias classifiers
Higher-order Logistic Regression
Issue Date2016
PublisherKluwer Academic Publishers
CitationMachine Learning 104: 151- 194 (2016)
AbstractThis 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.
Identifiersdoi: 10.1007/s10994-016-5574-8
issn: 1573-0565
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