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

Citation

Gheyas IA & Smith L (2010) A neural network-based framework for the reconstruction of incomplete data sets. 10th Brazilian Symposium on Neural Networks (SBRN2008), Salvador, Brazil, 26.10.2008-30.10.2008. Neurocomputing, 73 (16-18), pp. 3039-3065. https://doi.org/10.1016/j.neucom.2010.06.021

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.

Keywords
Missing values; Imputation; Single imputation; Multiple imputation; Generalized regression neural networks; Neural networks (Computer science); Data mining

Journal
Neurocomputing: Volume 73, Issue 16-18

People

Professor Leslie Smith

Emeritus Professor, Computing Science