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 flo- function representing the strength, certainty factor, and coverage factor of the decision rules.