A Hybrid Approach to Pattern Classification Using Neural Networks and Defeasible Argumentation

摘要

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c{sub}1,... c{sub}m modeling some concept C results as an output, such that every cluster a 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.