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A new measure of uncertainty based on knowledge granulation for rough sets. (English) Zbl 1162.68666

Summary: In rough set theory, accuracy and roughness are used to characterize uncertainty of a set and approximation accuracy is employed to depict accuracy of a rough classification. Although these measures are effective, they have some limitations when the lower/upper approximation of a set under one knowledge is equal to that under another knowledge. To overcome these limitations, we address in this paper the issues of uncertainty of a set in an information system and approximation accuracy of a rough classification in a decision table. An axiomatic definition of knowledge granulation for an information system is given, under which these three measures are modified. Theoretical studies and experimental results show that the modified measures are effective and suitable for evaluating the roughness and accuracy of a set in an information system and the approximation accuracy of a rough classification in a decision table, respectively, and have a much simpler and more comprehensive form than the existing ones.

MSC:

68T30 Knowledge representation
68T37 Reasoning under uncertainty in the context of artificial intelligence

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References:

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