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Techniques for Dealing with Missing Values in Feedforward Networks

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Vamplew, P and Clark, David and Adams, A and Muench, J (1996) Techniques for Dealing with Missing Values in Feedforward Networks. In: ACNN'96: The Seventh Australian Conference on Neural Networks, Canberra, ACT.

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Abstract

Missing or incomplete data, a common reality, causes problems for artificial neural networks. In this paper we investigate several methods for dealing with missing values in feedforward networks. Reduced networks, substitution, estimation and expanded networks are applied to three data sets. We find that data sets vary in their sensitivity to missing values, and that reduced networks and estimation are the most effective ways of dealing with them.

Item Type: Conference or Workshop Item (Paper)
Keywords: Neural networks, missing data, missing inputs
Date Deposited: 12 Aug 2004
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/45
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