基于模糊概念格的分类器

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI:10.1109/FSKD.2007.322

Wen Zhou, Zongtian Liu, Yan Zhao, Yu Zheng, Dong Xu, W. Liu, Ying Zhu

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引用次数: 0

摘要

大量的形式概念可以成为FCA应用的一个障碍。因此，开发有助于克服概念格的巨大尺寸问题的方法是一项重要的任务。提出了一种基于聚类的模糊概念格约简算法。最后，实验结果表明，概念格和分类器的压缩率显著提高，同时保持了概念格分类的准确性。

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Fuzzy Concept Lattice Based Classifier

The large collection of formal concepts can be a hedge of the application of FCA. Development of methods which help to overcome the problem of the huge size of concept lattice is thus an important task. This paper proposes clustering-based reduction algorithm for reducing the size of fuzzy concept lattices. At the end, experiment results show that the compression rate of the concept lattice and classifier is remarkable, while it preserves the accuracy of classification of concept lattice.

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Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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