A Hybrid Approach to Pattern Classification UsingNeural Networks and Defeasible Argumentation

May 2004

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

Volume

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

All Papers

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 modeling some concept C results as an output, such that every cluster ci is labeled as positive or negative. In such a setting clusters can overlap, and a new unlabeled 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 introduces a novel, hybrid approach to solve the above problem by combining a neural network N along with a background theory T specified in defeasible logic programming (DeLP) which models preference criteria for performing clustering.