A neural network-based framework for the reconstruction of incomplete data sets

Iffat A. Gheyas, Leslie S. Smith

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)3039-3065
Number of pages27
Issue number16-18
Early online date8 Aug 2010
Publication statusPublished - Oct 2010
Externally publishedYes


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