Incomplete-data classification using logistic regression

David Williams, Xuejun Liao, Ya Xue, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

37 Scopus citations

Abstract

A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the non-missing data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both expectation maximization (EM) and Variational Bayesian EM (VB-EM). Using widely available real data, we demonstrate the general advantage of the VB-EM GMM estimation for handling incomplete data, vis-à-vis the EM algorithm. Moreover, it is demonstrated that the approach proposed here is generally superior to standard imputation procedures.
Original languageEnglish (US)
Title of host publicationICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
Pages977-984
Number of pages8
StatePublished - Dec 1 2005
Externally publishedYes

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