Integrating Defeasible Argumentation with Fuzzy ART Neural Networks for Pattern Classification

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. Should the cluster ci be labelled as positive (negative), then the instance enew is regarded as positive (negative). 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 nondeterministically 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.