Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3106

Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A neural network-based framework for the reconstruction of incomplete data sets
Authors: Gheyas, Iffat A
Smith, Leslie
Contact Email: lss@cs.stir.ac.uk
Keywords: Missing values
Imputation
Single imputation
Multiple imputation
Generalized regression neural networks
Issue Date: Oct-2010
Publisher: Elsevier
Citation: Gheyas IA & Smith L (2010) A neural network-based framework for the reconstruction of incomplete data sets, Neurocomputing, 73 (16-18), pp. 3039-3065.
Abstract: The treatment of incomplete data is an important step in the pre-processing of data. We propose a novel nonparametric algorithm Generalized regression neural network Ensemble for Multiple Imputation (GEMI). We also developed a single imputation (SI) version of this approach-GESI. We compare our algorithms with 25 popular missing data imputation algorithms on 98 real-world and synthetic datasets for various percentage of missing values. The effectiveness of the algorithms is evaluated in terms of (i) the accuracy of output classification: three classifiers (a generalized regression neural network, a multilayer perceptron and a logistic regression technique) are separately trained and tested on the dataset imputed with each imputation algorithm, (ii) interval analysis with missing observations and (iii) point estimation accuracy of the missing value imputation. GEMI outperformed GESI and all the conventional imputation algorithms in terms of all three criteria considered.
Type: Journal Article
URI: http://hdl.handle.net/1893/3106
DOI Link: http://dx.doi.org/10.1016/j.neucom.2010.06.021
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Notes: 10th Brazilian Symposium on Neural Networks (SBRN2008)
Affiliation: University of Stirling
Computing Science - CSM Dept

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