Regression in the Presence Missing Data Using Ensemble Methods

We consider the problem of missing data, and develop ensemble-network models for handling the missing data. The proposed method is based on utilizing the inherent uncertainty of the missing records in generating diverse training sets for the ensemble's networks. The proposed method is based on...

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Bibliographic Details
Main Authors: Hassan, M.M., Atiya, A.F., El-Gayar, N., El-Fouly, R.
Format: Conference Proceeding
Language:English
Subjects:
Information technology
Learning systems
Linear regression
Machine learning algorithms
Maximum likelihood estimation
Neural networks
Parameter estimation
Statistics
Training data
Uncertainty
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Description
Summary:We consider the problem of missing data, and develop ensemble-network models for handling the missing data. The proposed method is based on utilizing the inherent uncertainty of the missing records in generating diverse training sets for the ensemble's networks. The proposed method is based on generating the missing values using their probability density. We repeat this procedure many time thereby creating several complete data sets. A network is trained for each of these data sets, therefore obtaining an ensemble of networks. Several variants are proposed, including the univariate approach and the multivariate approach, which differ in the way missing values are generated. Simulation results confirm the general superiority of the proposed methods compared to the conventional approaches.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2007.4371139