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Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification
Gómez, Sergio Alejandro; Chesñevar, Carlos Iván
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.
2004
Machine learning
Defeasible argumentation
Neural networks
Pattern classification
Xarxes neuronals (Informàtica)
open access
(c) Iberoamerican Science & Technology Education Consortium, 2004
article
Iberoamerican Science & Technology Education Consortium
         

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