Using artificial neural network ensembles to extract data content from noisy data

Authors:
Szymon K. Szukalski;Robert J. Cox;Patricia S. Crowther
Affiliations:
School of Information Sciences and Engineering, University of Canberra, ACT, Australia;School of Information Sciences and Engineering, University of Canberra, ACT, Australia;School of Information Sciences and Engineering, University of Canberra, ACT, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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Abstract

We have developed a technique to extract points that contain information from a sea of noisy data using an ensemble of Artificial Neural Networks. The technique is relatively simple to use and by using artificial data sets we demonstrate that it can extract a subset of the data that in effect has a higher signal to noise ratio than the original data. We assert that this technique is of practical use in the area of classification, although it does appear to lose points, particularly near the discriminator.