Evaluating the Performance of the Multilayer Perceptron as a Data Editing Tool

Abstract

Usually, the knowledge discovery process is developed using data sets which contain errors in the form of inconsistent values. The activity aimed at detecting and correcting logical inconsistencies in data sets is named as data editing. Traditional tools for this task, as the Fellegi-Holt methodology, require a heavy intervention of subject matter experts. This paper discusses a methodological framework for the development of an automated data editing process which can be accomplished by a general nonlinear approximation model, as an artificial neural network. We have performed and empirical evaluation of the performance of this approach over eight data sets, considering several hidden layer sizes and seven learning algorithms for the multilayer perceptron. The obtained results suggest that this approach offers a hopeful performance, providing a promising data cleaning tool.