DocumentCode :
1571445
Title :
A study of method for knowledge discovery on set-valued features
Author :
Liang, Sun ; Chongzhao, Han ; Xin, Kang
Author_Institution :
Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China
Volume :
4
fYear :
2004
Firstpage :
3050
Abstract :
A new method for classification on set-valued features is proposed and was used based on the adaptive subspace decomposition and separability index. In a high-dimensional original feature space, a few dimensions adapted for classification are selected from the subspaces. The classification rules are extracted from the decision information table based on the selected dimensions and binary relation about the set-valued features. An example of hyperspectral image classification was given, and an experimental investigation shows that it is an effective knowledge-based data fusion method.
Keywords :
data mining; decision tables; image classification; sensor fusion; visual databases; adaptive subspace decomposition; decision information table; hyperspectral image classification; knowledge discovery; knowledge-based data fusion method; separability index; set-valued features; Data mining; Hyperspectral imaging; Image classification; Sun; Variable speed drives;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1343079
Filename :
1343079
Link To Document :
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