A new method for measuring uncertainty and fuzziness in rough set theory

Jiye Liang; K. S. Chin; Chuangyin Dang; Richard C. M. Yam

doi:10.1080/0308107021000013635

A new method for measuring uncertainty and fuzziness in rough set theory

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

Original language	English
Pages (from-to)	331-342
Journal	International Journal of General Systems
Volume	31
Issue number	4
DOIs	https://doi.org/10.1080/0308107021000013635
Publication status	Published - 2002

Research Keywords

Data analysis
Fuzziness
Information entropy
Rough classification
Rough sets
Uncertainty

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10.1080/0308107021000013635

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title = "A new method for measuring uncertainty and fuzziness in rough set theory",

abstract = "Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.",

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author = "Jiye Liang and Chin, \{K. S.\} and Chuangyin Dang and Yam, \{Richard C. M.\}",

year = "2002",

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journal = "International Journal of General Systems",

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T1 - A new method for measuring uncertainty and fuzziness in rough set theory

AU - Liang, Jiye

AU - Chin, K. S.

AU - Dang, Chuangyin

AU - Yam, Richard C. M.

PY - 2002

Y1 - 2002

N2 - Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

AB - Based on the complement behavior of information gain, a new definition of information entropy is proposed along with its justification in rough set theory. Some properties of this definition imply those of Shannon's entropy. Based on the new information entropy, conditional entropy and mutual information are then introduced and applied to knowledge bases. The new information entropy is proved to also be a fuzzy entropy.

KW - Data analysis

KW - Fuzziness

KW - Information entropy

KW - Rough classification

KW - Rough sets

KW - Uncertainty

UR - https://www.scopus.com/pages/publications/1842674114

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-1842674114&origin=recordpage

U2 - 10.1080/0308107021000013635

DO - 10.1080/0308107021000013635

M3 - RGC 21 - Publication in refereed journal

SN - 0308-1079

VL - 31

SP - 331

EP - 342

JO - International Journal of General Systems

JF - International Journal of General Systems

IS - 4

ER -

A new method for measuring uncertainty and fuzziness in rough set theory

Abstract

Research Keywords

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