Title :
Battlefield Reconnaissance Intelligence Processing Based on Rough Sets Theory
Author :
Xiong, Li ; Sheng, Dang
Author_Institution :
Acad. of Armored Force Eng., Beijing
Abstract :
In order to raise the efficiency, automatization and intelligentization of battlefield reconnaissance intelligence processing, intelligence processing system is discussed and feature reduction algorithm based on rough sets theory is adopted to extract feature information in battlefield reconnaissance intelligence processing, so that the intelligence processing objects are optimized. The decision tables for each failure source are built and the analysis rules rooting in rough sets reduction are applied to carry through intelligent analysis for the system. The cases studied show that rough sets method can lighten the work burden in feature selection and afford advantage for autonomic learning and decision-making during intelligent analysis.
Keywords :
decision making; learning (artificial intelligence); military computing; rough set theory; autonomic learning; battlefield reconnaissance intelligence processing; decision making; decision tables; feature information extraction; feature reduction algorithm; rough sets theory; Data mining; Decision making; Failure analysis; Feature extraction; Information systems; Intelligent systems; Reconnaissance; Rough sets; Battlefield Reconnaissance; Intelligence Processing; Reduction; Rough Sets;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4347483
