Title :
A Novel Reduction Algorithm Based on Expert Knowledge
Author :
Yuan, Junpeng ; Su, Jie ; Su, Cheng
Author_Institution :
Inst. of Sci. & Tech. of Inf. of China, Beijing
Abstract :
For complex systems and some new technology fields, simply rely on machines learning, the results are not reliable. Absorb the expert knowledge, will help us to more accurately grasp the status and the development of the complex or new fields. This paper presents a novel reduction algorithm that takes into account the knowledge of experts. The attributes set is divided into two subset according to the scores of the expert knowledge: the set of attributes with decisive expert knowledge and the set of attributes with experts´ knowledge for reference, then a novel man-machine cooperative intelligent reduction algorithm (IRAEK, Intelligent Reduction Algorithm based on Expert Knowledge) is proposed to find the minimum reduction based on the different scores of expert knowledge. Finally, the empirical analysis result on the Micro-electromechanical Systems (MEMS) field shows that the IRAEK algorithm is feasible and efficient.
Keywords :
expert systems; learning (artificial intelligence); set theory; expert knowledge; machine learning; man-machine cooperative intelligent reduction algorithm; micro-electromechanical system; Conference management; Embedded software; Financial management; Information systems; Knowledge management; Man machine systems; Microelectromechanical systems; Micromechanical devices; Set theory; Software algorithms; Decisive Expert Knowledge; Experts´ Knowledge for Reference; Man-Machine Cooperative; Reduction Algorithm; Rough Set Theory;
Conference_Titel :
Embedded Software and Systems, 2009. ICESS '09. International Conference on
Conference_Location :
Zhejiang
Print_ISBN :
978-1-4244-4359-8
DOI :
10.1109/ICESS.2009.45