DocumentCode
2121155
Title
Notice of Violation of IEEE Publication Principles
Influence diagram based on rough set theory
Author
Yueling Zhao ; Hui Jin ; Lihong Wang ; Shuang Wang
Author_Institution
Coll. of Electr. Eng., Liao Ning Univ. of Technol., Jinzhou, China
fYear
2010
fDate
29-31 July 2010
Firstpage
1707
Lastpage
1710
Abstract
Notice of Violation of IEEE Publication Principles
"Influence Diagram Based on Rough Set Theory"
by: ZHAO Yueling, JIN Hui, WANG Lihong, WANG Shuang
in the Proceedings of the 29th Chinese Control Conference July 29-31, 2010, Beijing, China.
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Intelligent Decision Support Based on Influence Diagrams with Rough Sets",
by Chia-Hui Huang, Han-Ying Kao, and Han-Lin Li, in A. An et al. (Eds.): RSFDGrC 2007, LNAI 4482, pp. 518-525, 2007, Springer-Verlag
In conventional influence diagrams, the numerical models of uncertainty and imprecise knowledge from large-scaled data set is involved in the systems, the suitability of probability distributions is questioned. The influence diagrams model based on rough sets are proposed in this paper. In the framework, the causal relationships among the nodes and the decision rules are expressed with rough set theory. The main objectives of this paper are to describe how rough sets theory can be applied to develop the influence diagrams which combine the rough set decision rules. Rough set theory provides a basis for extracting the knowledge and expressing the dependency among nodes in the influence diagrams. In order to represent the ontology, a directed acyclic graph is defined with influence diagram based on rough set. The links(arcs) between nodes are flow funct- on representing the strength, certainty factor, and coverage factor of the decision rules.
"Influence Diagram Based on Rough Set Theory"
by: ZHAO Yueling, JIN Hui, WANG Lihong, WANG Shuang
in the Proceedings of the 29th Chinese Control Conference July 29-31, 2010, Beijing, China.
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Intelligent Decision Support Based on Influence Diagrams with Rough Sets",
by Chia-Hui Huang, Han-Ying Kao, and Han-Lin Li, in A. An et al. (Eds.): RSFDGrC 2007, LNAI 4482, pp. 518-525, 2007, Springer-Verlag
In conventional influence diagrams, the numerical models of uncertainty and imprecise knowledge from large-scaled data set is involved in the systems, the suitability of probability distributions is questioned. The influence diagrams model based on rough sets are proposed in this paper. In the framework, the causal relationships among the nodes and the decision rules are expressed with rough set theory. The main objectives of this paper are to describe how rough sets theory can be applied to develop the influence diagrams which combine the rough set decision rules. Rough set theory provides a basis for extracting the knowledge and expressing the dependency among nodes in the influence diagrams. In order to represent the ontology, a directed acyclic graph is defined with influence diagram based on rough set. The links(arcs) between nodes are flow funct- on representing the strength, certainty factor, and coverage factor of the decision rules.
Keywords
diagrams; directed graphs; rough set theory; statistical distributions; causal relationship; certainty factor; coverage factor; directed acyclic graph; flow function; imprecise knowledge; influence diagram; large-scaled data set; numerical model; probability distribution; rough set decision rules; rough set theory; uncertainty; Approximation methods; Flow graphs; Numerical models; Probabilistic logic; Rough sets; Uncertainty; Certainty Factor; Coverage Factor; Influence Diagrams; Rough Set;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2010 29th Chinese
Conference_Location
Beijing
Print_ISBN
978-1-4244-6263-6
Type
conf
Filename
5573964
Link To Document