Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs with Missing Values

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dc.creatorNelwamondo, F. V.
dc.creatorMarwala, T.
dc.date2007-05-08
dc.date.accessioned2026-07-07T07:59:58Z
dc.date.available2026-07-07T07:59:58Z
dc.descriptionAn ensemble based approach for dealing with missing data, without predicting or imputing the missing values is proposed. This technique is suitable for online operations of neural networks and as a result, is used for online condition monitoring. The proposed technique is tested in both classification and regression problems. An ensemble of Fuzzy-ARTMAPs is used for classification whereas an ensemble of multi-layer perceptrons is used for the regression problem. Results obtained using this ensemble-based technique are compared to those obtained using a combination of auto-associative neural networks and genetic algorithms and findings show that this method can perform up to 9% better in regression problems. Another advantage of the proposed technique is that it eliminates the need for finding the best estimate of the data, and hence, saves time.
dc.description7 pages
dc.identifierhttps://arxiv.org/abs/0705.1031
dc.identifierhttp://arxiv.org/abs/0705.1031
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128568
dc.subjectArtificial Intelligence
dc.titleFuzzy Artmap and Neural Network Approach to Online Processing of Inputs with Missing Values
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