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Managing Category Proliferation in Fuzzy ARTMAP Caused by Overlapping Classes

机译：管理由重叠类引起的模糊ARTMAP中的类别扩散

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摘要

This paper addresses the difficulties brought about by overlapping classes in fuzzy ARTMAP (FAM). Training with such data leads to category proliferation, and classification is made difficult not only by the large number of categories but also the fact that such data can belong to either class. In this paper, changes were proposed to allow more than one class to be predicted during classification, and a number of modifications were explored to reduce the number of categories. The excessive creation of small categories was suppressed with the implementation of the modifications, and the predictive accuracy improved despite the significant reduction in number of categories. No major changes needed to be made to the FAM architecture.
机译：本文解决了模糊ARTMAP（FAM）中重叠类所带来的困难。使用此类数据进行训练会导致类别激增，不仅由于类别众多，而且使此类数据可以属于任一类别这一事实也使分类变得困难。在本文中，提出了一些更改以允许在分类期间预测一个以上的类别，并进行了许多修改以减少类别的数量。修改的实施抑制了小类别的过度创建，尽管类别数量显着减少，但预测准确性有所提高。 FAM体系结构无需进行重大更改。

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