NDLI: Robust Bayesian Classification with Incomplete Data

Content Provider	Springer Nature Link
Author	Zhang, Xunan Song, Shiji Wu, Cheng
Copyright Year	2012
Abstract	In this paper, we address the Bayesian classification with incomplete data. The common approach in the literature is to simply ignore the samples with missing values or impute missing values before classification. However, these methods are not effective when a large portion of the data have missing values and the acquisition of samples is expensive. Motivated by these limitations, the expectation maximization algorithm for learning a multivariate Gaussian mixture model and a multiple kernel density estimator based on the propensity scores are proposed to avoid listwise deletion (LD) or mean imputation (MI) for solving classification tasks with incomplete data. We illustrate the effectiveness of our proposed algorithms on some artificial and benchmark UCI data sets by comparing with LD and MI methods. We also apply these algorithms to solve the practical classification tasks on the lithology identification of hydrothermal minerals and license plate character recognition. The experimental results demonstrate their good performance with high classification accuracies.
Starting Page	170
Ending Page	187
Page Count	18
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	5
Issue Number	2
e-ISSN	18669964
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-09-21
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian classification Incomplete data EM algorithm Propensity scores Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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