Sample Papers on Analysis of Information Table with Added Semantics
(Click paper title to download).
Information table with with added semantics about attributes
One can added semantics about attributes with respect to their
importance. In this group of papers, we focus on user
ordering (or user preferences) of attributes, which provides a
qualitaive representation of the importance of attributes.
User preferences of attributes are useful in the construction
Yao, Y.Y., Zhao, Y., Wang, J., and Han, S.,
A model of machine learning based on user preference of attributes,
Rough Sets and Current Trends in Computing, 5th International
Conference, RSCTC 2006, Proceedings,
LNAI 4259, pp. 587-596, 2006.
Information table with with added semantics about attribute values
Classical rough set model only consider the trivial equality
relation "=" on attribute values. In general, one may consider
other semantics on attribute values. Three specific types of
semantics can be considered, namely, any binary relation,
order relation, and neighborhood systems.
Analysis of Information Table with Added Relations on Attribute Values
Yao, Y.Y. and Wong, S.K.M. Generalization of rough sets using relationships between attribute values,
Proceedings of the 2nd Annual Joint Conference on Information Sciences,
Wrightsville Beach, North Carolina, USA, September 28 - October 1, 1995,
P.P. Wang (Ed.), pp. 30-33.
In this paper, an arbitrary binary relation is first defined on
the domain of each attribute. It extends the commonly used formulation
based on an equivalence relation defined by equality. The
results are also included in the following book chapter (Section~2.3).
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. and Liau, C.-J.
A generalized decision logic language for granular computing,
FUZZ-IEEE'02 in The 2002 IEEE World Congress on
Honolulu, Hawaii, USA, May 12-17, 2002, pp. 1092-1097.
In this paper, a generalized decision logic language is introduced.
The language include the treatment of binary relations on the domain
of an attribute.
Analysis of Information Table with Orderings on Attribute Values
The following group of papers deals with a particular types
of binary relations (i.e., orderings). The problem of discovering of ordering
rules is addressed.
(Ranking, ordinal classification or sorting problems)
Yao, Y.Y., Zhou, B., and Chen Y.H.
Interpreting low and high order rules: a granular computing approach,
Proceedings of International Conference on Rough Sets and Emerging Intelligent
System Paradigms (RSEISP'07), LNAI 4585, pp. 371-380, 2007.
Mining high order decision rules,
in: Rough Set Theory and Granular Computing, Inuiguchi, M.,
Hirano, S. and Tsumoto, S. (Eds.), Springer, Berlin, pp. 125-135, 2003.
Yao, Y.Y. and Sai, Y., On mining ordering rules,
Frontiers in Artificial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings,
Lecture Notes in Computer Science 2253, Terano, T., Nishida, T., Namatame,
A., Tsumoto, S., Ohsawa, Y. and Washio, T. (Eds.), Springer, Berlin, 316-321,
2001. A long version entitled "Mining ordering rules using rough set theory"
also appeared at Proceedings of International Workshop on Rough Set
Theory and Granular Computing, Matsue, Shimane, Japan, May 20-22, 2001,
of International Rough Set Society, Vol. 5, No. 1-2, Hirano, S., Inuiguchi,
M., and Tsumoto, S. (Eds.), pp. 99-106.
Y. Sai, Yao, Y.Y., and N. Zhong
Data analysis and mining in ordered information tables,
Proceedings of the 2001 IEEE International Conference on Data Mining ,
pp. 497-504, 2001.
Evaluation measures of ranking, ordinal classification, and sorting systems
This group of papers concern measure for evaluating ranking in information
retrieval. The results are applicable ordinal classification or sorting system in general.
Measuring retrieval performance based on user preference of documents,
Journal of the American Society for Information Science,
Vol. 46, No. 2, pp. 133-145, 1995.
Zhou, B. and Yao, Y.Y.
Evaluating information retrieval system performance based on user preference,
Journal of Intelligent Information Systems (JIIS),
Volume 34, Issue 3, pp. 227-248, 2010.
Analysis of Information Table with Neigbhorhood Systems on Attribute Values
Information table with neighborhood semantics,
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II,
pp. 108-116, 2000.
Neighborhood systems and approximate retrieval,
Information Sciences, Vol. 176, No. 23, 3431-3452, 2006.
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