DocumentCode :
2069015
Title :
An improved attribute importance degree algorithm based on rough set
Author :
Lisha Kong ; Mai, Jianying ; Mei, Shengkai ; Fan, Yongjian
Author_Institution :
Simulation Training Center, Army Aviation Inst. of PLA, Beijing, China
Volume :
1
fYear :
2010
fDate :
10-12 Dec. 2010
Firstpage :
122
Lastpage :
126
Abstract :
In order to improve the efficiency of attribute importance degree algorithm, the thesis brings forward an new and efficient algorithm. It sorts the decision table by the radix sorting based on distribution and statistics at first and then calculates the equivalence class, so it reduces the time complexity of equivalence class algorithm from O(|C∥U|2) to O(|C∥U|). It is on the basis of the improved equivalence class algorithm that the improved attribute importance degree algorithm is presented. The analysis of an example and the result of experiments prove that the result of calculating attribute importance degree by the traditional and the improved attribute importance degree algorithm are completely same, but the improved attribute importance degree algorithm brought forward by the thesis is more efficient.
Keywords :
equivalence classes; rough set theory; statistical distributions; decision table; equivalence class algorithm; improved attribute importance degree algorithm; radix sorting; rough set; statistics; Artificial neural networks; Glass; Iris; Vehicles; Rough Set; the attribute importance degree; the equivalence class; time complexity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6788-4
Type :
conf
DOI :
10.1109/PIC.2010.5687418
Filename :
5687418
Link To Document :
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