missingvaluesacnn96.pdf (208.71 kB)
Techniques for Dealing with Missing Values in Feedforward Networks
conference contribution
posted on 2023-05-26, 09:55 authored by Vamplew, P, Clark, David, Adams, A, Muench, JMissing 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.
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Publication status
- Published
Event title
ACNN'96: The Seventh Australian Conference on Neural NetworksEvent Venue
Canberra, ACTRepository Status
- Open
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