DocumentCode
2657924
Title
Notice of Retraction
Attribute reduction based on rough set and AHP
Author
Lanchun Wang ; Jianguo Wang ; Liying Liu
Author_Institution
Dept. of Comput. Sci., Xinzhou Teachers Univ., Xinzhou, China
Volume
3
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper proposed a reasonable evaluation model with the decision-making attribute as the upper factor and the condition attribute as the lower factor, considering both the advantage of reduction in processing massive data and the advantage of AHP in decision-making. AHP has been used in reduction for the rules extracted. The attribute reduction algorithm utilized to improve the reduce of the magnanimous data redundancy. With the aid of the attribute importance in Rough Set theory, it compensates for the AHP evaluation factor subjective factors. The algorithm model omittes the process of extracting nuclear, analyses the rules extracted quantitatively, and accomplishes the reduction of the massive data attributes and rules. Effectiveness of the algorithm has been proved by examples.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper proposed a reasonable evaluation model with the decision-making attribute as the upper factor and the condition attribute as the lower factor, considering both the advantage of reduction in processing massive data and the advantage of AHP in decision-making. AHP has been used in reduction for the rules extracted. The attribute reduction algorithm utilized to improve the reduce of the magnanimous data redundancy. With the aid of the attribute importance in Rough Set theory, it compensates for the AHP evaluation factor subjective factors. The algorithm model omittes the process of extracting nuclear, analyses the rules extracted quantitatively, and accomplishes the reduction of the massive data attributes and rules. Effectiveness of the algorithm has been proved by examples.
Keywords
data mining; data reduction; decision making; rough set theory; AHP evaluation factor; analytical hierarchy process; attribute reduction; condition attribute; data mining; data redundancy; decision rule; decision-making attribute; rough set theory; rule extraction; Computer network management; Data security; Databases; Information analysis; Management information systems; Object oriented modeling; Packaging; Project management; Space technology; Technology management; AHP; attribute rduction; evaluation factors; importance; rough set;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485867
Filename
5485867
Link To Document