A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method

Authors:
Julián Luengo;Salvador García;Francisco Herrera
Affiliations:
Department of Computer Science and Artificial Intelligence, CITIC-University of Granada, 18071, Granada, Spain;Department of Computer Science, University of Jaen, 23071, Jaen, Spain;Department of Computer Science and Artificial Intelligence, CITIC-University of Granada, 18071, Granada, Spain
Venue:
Neural Networks
Year:
2010

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Abstract

The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. Several methods have been proposed to treat missing data and the one used more frequently is the imputation of the Missing Values of an instance. In this paper, we analyze the improvement of performance on Radial Basis Function Networks by means of the use of several imputation methods in the classification task with missing values. The study has been conducted using data sets with real Missing Values, and data sets with artificial Missing Values. The results obtained show that EventCovering offers a very good synergy with Radial Basis Function Networks. It allows us to overcome the negative impact of the presence of Missing Values to a certain degree.