The rule-matching algorithm of decision tree attribute reduction

Author

Li, Yan ; Li, Fa-chao ; Li, Yun-hong

Author_Institution

Sch. of Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang

Volume

2

fYear

2008

fDate

30-31 Aug. 2008

Firstpage

868

Lastpage

872

Abstract

The attribute reduction of information system can improve the accuracy of knowledge discovery, machine learning, etc. and it also can improve the efficiency. This paper proposes an attribute testing reduction algorithm, the algorithm can make the information system retain as few as attributes under the condition that maintains the original style, it can not only save much time for the later system handling, but reduce time complexity of algorithm and improve the computation precision.

Keywords

data mining; decision trees; learning (artificial intelligence); rough set theory; computation precision; decision tree attribute reduction; information system; knowledge discovery; machine learning; rule-matching algorithm; time complexity; Classification tree analysis; Decision trees; Machine learning; Machine learning algorithms; Management information systems; Partitioning algorithms; Pattern analysis; Pattern recognition; Set theory; Wavelet analysis; Attribute reduction; Decision tree; Rough set; Rule matching degree; Threshold;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-2238-8

Electronic_ISBN

978-1-4244-2239-5

Type

conf

DOI

10.1109/ICWAPR.2008.4635898

Filename

4635898