K. Lees, S. O'Keefe, and J. Austin
Advanced Computer Architectures Group
Department of Computer Science
University of York
Heslington
YORK YO10 5DD
UK
This paper describes a novel application of a binary neural network technology to the important practical task of statistical data editing and imputation. Editing and Imputation is used to improve data quality by most National Statistical Institutes and some commercial organisations. The paper describes how the AURA (Advanced Uncertain Reasoning Architecture) high-speed pattern matching system can be used to find a subset of data records similar to a given record. This can accelerate the processing of records with missing values and errors, allowing slower, conventional Euclidean distance based techniques to be used in the post-processing stage. A central part of the AURA system is the CMM (Correlation Matrix Memory) neural network. A binary version of the CMM is described, which has been studied at University of York for over 15 years, and some preliminary edit and imputation results are presented. The work at York is being carried out as part of the Euredit project. Euredit is supported under the European 5th Framework Programme, has 12 partners from 7 European states, and is investigating and comparing new and existing methods for edit and imputation, including neural network methods such as MLP, SVM, SOM, and CMM. The project is evaluating the performance of each method against a range of common datasets.