Handling incomplete data using evolution of imputation methods

Authors:
Pawel Zawistowski;Maciej Grzenda
Affiliations:
Warsaw University of Technology, Faculty of Electronics and Information Technologies, Institute of Electronic Systems, Warsaw, Poland;Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

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Abstract

In this paper new approach to treat incomplete data has been proposed. It has been based on the evolution of imputation strategies built using both non-parametric and parametric imputation methods. Genetic algorithms and multilayer perceptrons have been applied to develop a framework for constructing the imputation strategies addressing multiple incomplete attributes. Furthermore we evaluate imputation methods in the context of not only the data they are applied to, but also the model using the data. The accuracy of classification on data sets completed using obtained imputation strategies has been described. The results outperform the corresponding results calculated for the same data sets completed using standard strategies.