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ICML
2005
IEEE

Incomplete-data classification using logistic regression

14 years 4 months ago
Incomplete-data classification using logistic regression
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 nonmissing 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-`a-vis the EM algorithm. Moreover, it is demonstrated that the approach proposed here is generally superior to standard imputation procedures.
David Williams, Xuejun Liao, Ya Xue, Lawrence Cari
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors David Williams, Xuejun Liao, Ya Xue, Lawrence Carin
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