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Title:
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Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification
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Author:
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Gómez, Sergio Alejandro; Chesñevar, Carlos Iván
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Notes:
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Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering. |
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Subject(s):
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-Machine learning
-Defeasible argumentation
-Neural networks
-Pattern classification
-Xarxes neuronals (Informàtica) |
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Rights:
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(c) Iberoamerican Science & Technology Education Consortium, 2004
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Document type:
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article
publishedVersion |
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Published by:
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Iberoamerican Science & Technology Education Consortium
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