Robust weighted kernel logistic regression in imbalanced and rare events data

DOI10.1016/j.csda.2010.06.014zbMath1247.62190OpenAlexW1988056000MaRDI QIDQ452553

Theodore B. Trafalis, Maher Maalouf

Publication date: 15 September 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/11244/319134

zbMATH Keywords

classificationlogistic regressionkernel methodsendogenous samplingtruncated Newton

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data ⋮ Matrix sketching for supervised classification with imbalanced classes ⋮ Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function ⋮ On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking ⋮ Methods for correcting inference based on outcomes predicted by machine learning ⋮ Loan pricing under estimation risk ⋮ Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data ⋮ Performance of prior and weighting bias correction methods for rare event logistic regression under the influence of sampling bias ⋮ Improving predictive accuracy of logistic regression model using ranked set samples ⋮ Kernel logistic regression using truncated Newton method ⋮ More efficient logistic analysis using moving extreme ranked set sampling ⋮ Further improving the performance of logistic regression analysis using double extreme ranking ⋮ Bankruptcy prediction: evidence from Vietnam

Uses Software

• UCI-ml
• fda (R)
• LIBSVM

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