An efficient fusion approach to rule extraction based on rough set theory and particle swarm optimization and its application

Rule extraction is viewed as an important pretreatment step for machine learning and data mining. In allusion to the shortcomings of the rough set method for rule extraction in systems with incomplete fault diagnosis, in this paper we propose a novel fusion approach based on rough set theory and particle swarm optimization for rule extraction (RSTPSORE) in order to improve the diagnostic robustness and accuracy. In the proposed method, first, an iterative linear subsection interpolation completion method is used to achieve completion of the fault diagnosis process. Then rough set theory is used to find a subset which can preserve the meaning of the attributes. Particle swarm optimization is used to discover the best rule within the flying subset space. To verify the efficiency of the proposed method, experiments are carried out on the collected data in a centralized control substation incomplete fault diagnosis system. The results indicate that the proposed method can provide an efficient solution to finding a minimal subset.

[1]  Tianyou Chai,et al.  Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process , 2007 .

[2]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  Masahiro Inuiguchi.  Attribute Reduction in Variable Precision Rough Set Model , 2006, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  R. J. Kuo,et al.  Application of particle swarm optimization to association rule mining , 2011, Appl. Soft Comput..

[5]  Duoqian Miao,et al.  A rough set approach to feature selection based on ant colony optimization , 2010, Pattern Recognit. Lett..

[6]  Shusaku Tsumoto,et al.  Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model , 2004, Inf. Sci..

[7]  Li Pheng Khoo,et al.  Feature extraction using rough set theory and genetic algorithms--an application for the simplification of product quality evaluation , 2002 .

[8]  Wang Rong,et al.  A genetic algorithm methodology for data mining and intelligent knowledge acquisition , 2001 .

[9]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[10]  Wei Guo,et al.  Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms , 2010 .

[11]  Wei-Chang Yeh,et al.  Feature selection with Intelligent Dynamic Swarm and Rough Set , 2010, Expert Syst. Appl..

[12]  S. N. Omkar,et al.  Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation , 2008, Eng. Appl. Artif. Intell..

[13]  Qunxiong Zhu,et al.  Rough set-based heuristic hybrid recognizer and its application in fault diagnosis , 2009, Expert Syst. Appl..

[14]  C-C Yang,et al.  A particle swarm optimization-based time-scaling method for quasi-time-optimal control of rigid spacecraft along specified paths , 2008 .