A new method for measuring uncertainty and fuzziness in rough set theory
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 331-342 |
| Journal / Publication | International Journal of General Systems |
| Volume | 31 |
| Issue number | 4 |
| Publication status | Published - 2002 |
Link(s)
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.
Research Area(s)
- Data analysis, Fuzziness, Information entropy, Rough classification, Rough sets, Uncertainty
Citation Format(s)
A new method for measuring uncertainty and fuzziness in rough set theory. / Liang, Jiye; Chin, K. S.; Dang, Chuangyin; Yam, Richard C. M.
In: International Journal of General Systems, Vol. 31, No. 4, 2002, p. 331-342.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
