Yao, Y.Y. and Yao, B.X.,
Covering based rough set approximations,
Information Sciences, Vol. 200, 91-107, 2012.
Highlights:
This paper reviews main results on covering based rough before 2000. A
common framework is proposed based on element-based definition, granule-based
definition and subsystem based definition of rough sets. Over twenty (20) pairs
of dual lower and upper approximations are examined.
Yao, Y.Y.,
On generalizing rough set theory,
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing,
Proceedings of the 9th International Conference (RSFDGrC 2003),
LNAI 2639, pp. 44-51, 2003.
Highlights:
This paper proposes a framework for studying generalized rough sets in
three directions. It expresses Pawlak rough set approximations in
three forms: a) element-based definition, b) granule-based definiton,
and c) subsystem-based definiton. The three definitions provide
hints to generalizing rough sets in three directions: a) generalized
rough sets by using arbitrary binary relations, b) generalized rough
sets by using coverings, and c) generalized rough sets by using other
mathematical structures, including Boolean algebras, closure systems,
topological spaces, lattices, and posets.
Yao, Y.Y.,
Generalized rough set models,
in: Rough Sets in Knowledge Discovery,
Polkowski, L. and Skowron, A. (Eds.),
Physica-Verlag, Heidelberg, pp. 286-318, 1998.
Highlights:
This paper reviews main reults on generalization rough sets before 1998.
Yao, Y.Y.,
On generalizing Pawlak approximation operators,
Rough Sets and Current Trends in Computing, Proceedings of the First
International Conference, RSCTC'98, LNAI 1424,
pp. 298-307, 1998.
Highlights:
This paper examines subsystem-based generalizations of rough sets.
Yao, Y.Y. and Lin, T.Y., Generalization of rough
sets using modal logic, Intelligent Automation and Soft Computing,
An International Journal, Vol. 2, No. 2, pp. 103-120, 1996.
Highlights:
By drawing results from modal logic, this paper discusses many generalized
models of rough sets by using an arbitrary binary relation. Five properties
of a binary relation is considered: serial, reflexivity, symmetry, transitivity,
and Euclidean. Different combinations lead to different rough set models.
A total of fifteen (15) distinct models are considered.
Two review papers
Yao, Y.Y., Wong, S.K.M., and Lin, T.Y.,
A review of rough set models,
in: Rough Sets and Data Mining: Analysis for
Imprecise Data, Lin, T.Y. and Cercone, N. (Eds.), Kluwer Academic Publishers,
Boston, pp. 47-75, 1997.
Yao, Y.Y.,
Generalized rough set models,
in: Rough Sets in Knowledge Discovery,
Polkowski, L. and Skowron, A. (Eds.),
Physica-Verlag, Heidelberg, pp. 286-318, 1998.
Bayesian rough sets, probabilisrtic rough sets
Yao, Y.Y. and Zhou, B.,
Naive Bayesian Rough Sets,
Proceedings of RSKT 2010, LNAI 6401, pp. 719-726, 2010.
Zhao, Y., Luo, F., Wong, S.K.M. and Yao, Y.Y.,
A general definition of an attribute reduct,
Rough Sets and Knowledge Technology,
Second International Conference, RSKT 2007, Proceedings,
LNAI 4481, pp. 101-108, 2007.
Yao, Y.Y., Zhao, Y. and Wang, J.,
On reduct construction algorithms,
Rough Sets and Knowledge Technology, First International Conference,
RSKT 2006, Proceedings,
LNAI 4062, pp. 297-304, 2006.
Rough sets, belief functions, and interval probability
Yao, Y.Y.
A comparison of two interval-valued probabilistic reasoning methods,
Proceedings of the 6th International Conference on Computing and
Information, Peterborough, Ontario, Canada, May 26-28, 1994,
Special issue of Journal of Computing and
Information, Vol. 1, No. 1, pp. 1090-1105 (paper number D6), 1995.
