文章基本信息

标题：Integrating Defeasible Argumentation with Fuzzy ART Neural Networks for Pattern Classification
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作者：S. A. Gómez ; C. I. Chesñevar
期刊名称：Journal of Computer Science and Technology
印刷版ISSN：1666-6046
电子版ISSN：1666-6038
出版年度：2004
卷号：4
期号：1
出版社：Iberoamerican Science & Technology Education Consortium
摘要：Many classification systems rely on clustering techniquesin which a collection of training examples is provided as aninput, and a number of clusters c1, . . . cmmodelling someconcept C results as an output, such that every cluster ciislabelled as positive or negative. Given a new, unlabelledinstance enew, the above classification is used to determineto which particular cluster cithis new instance belongs. Insuch a setting clusters can overlap, and a new unlabelledinstance can be assigned to more than one cluster with con-.icting labels. In the literature, such a case is usually solvednon-deterministically by making a random choice. This pa-per presents a novel, hybrid approach to solve this situationby combining a neural network for classification along witha defeasible argumentation framework which models pref-erence criteria for performing clustering
关键词：Machine Learning; Defeasible Argumenta-;tion; Neural networks; Pattern Classification