DocumentCode
3479038
Title
Notice of Retraction
Algorithm of constructing decision tree based on rough set theory
Author
Baowei Song ; Chunxue Wei
Author_Institution
Sch. of Comput. & Commun. Eng., Zheng Zhou Univ. of Light Ind., Zheng Zhou, China
Volume
2
fYear
2010
fDate
12-13 June 2010
Firstpage
300
Lastpage
302
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.
Problem of classification is the main research target of many algorithms in machine learning and data mining. Of all the algorithms, decision tree is more preferred by researchers due to its clarity and readability. Attribute of little value domain is the important feature of training dataset of decision trees. Based on this, this paper presents a new approach to construct decision tree after reducing dimension and compressing data set. Experiment shows that the algorithm proposed in this paper improves the efficiency in real applications compared with traditional algorithms.
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.
Problem of classification is the main research target of many algorithms in machine learning and data mining. Of all the algorithms, decision tree is more preferred by researchers due to its clarity and readability. Attribute of little value domain is the important feature of training dataset of decision trees. Based on this, this paper presents a new approach to construct decision tree after reducing dimension and compressing data set. Experiment shows that the algorithm proposed in this paper improves the efficiency in real applications compared with traditional algorithms.
Keywords
data mining; decision trees; learning (artificial intelligence); pattern classification; rough set theory; classification problem; compressed data set; data mining; decision tree; machine learning; rough set theory; Educational institutions; Glass; Iris; Data Compression; Decision Trees; Information Gain; Rough Set Theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Communication Technologies in Agriculture Engineering (CCTAE), 2010 International Conference On
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6944-4
Type
conf
DOI
10.1109/CCTAE.2010.5544400
Filename
5544400
Link To Document