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Semantic distance between vague concepts in a framework of modeling with words. (English) Zbl 1418.68216

Summary: Effectively measuring the similarity or dissimilarity of two vague concepts plays a key step in reasoning and computing with vague concepts. In this paper, we define semantic distances between data instances and vague concepts based on modeling vagueness in a framework called label semantics. We also propose two clustering methods based on these semantic distances, which can cluster data instances and vague concepts simultaneously. To evaluate our approach, we conduct several experimental studies on three datasets including Corel images and labels, Reuters-21578, and TDT2. It is illustrated that the proposed distances have the ability to effectively evaluate semantic similarities between data instances and vague concepts.

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

LIBSVM; LFOIL; LMNN; PRMLT
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Full Text: DOI

References:

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