Knowledge extraction algorithm for variances handling of CP using integrated hybrid genetic double multi-group cooperative PSO and DPSO

Journal of Medical Systems

Volumn 36, Issue 2, 2012, Pages 979-994

  Du, Gang ^a   Jiang, Zhibin ^a   Diao, Xiaodi ^b   Yao, Yang ^c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Shanghai Putuo District Central Hospital   (China)

^c Shanghai No 6 People's Hospital   (China)

Author keywords

Clinical pathway (CP); Data mining; Osteosarcoma; Particle swarm optimization algorithm; Rule extraction; Variances handling

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CLINICAL PATHWAY; DATA MINING; EVOLUTIONARY ALGORITHM; GENETIC ALGORITHM; HUMAN; MATHEMATICAL MODEL; OSTEOSARCOMA; PARTICLE SWARM OPTIMIZATION ALGORITHM; PATIENT CARE; RULE EXTRACTION ALGORITHM; SOCIAL BEHAVIOR; METHODOLOGY; ORGANIZATION AND MANAGEMENT; REGISTER; STATISTICS; VALIDATION STUDY;

ALGORITHMS; CRITICAL PATHWAYS; DATA MINING; HUMANS; OSTEOSARCOMA; REGISTRIES;

EID: 84863200616     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9562-4     Document Type: Article

References (36)

1. Matsumoto, K., Tanioka, T., Osaka, K., et al., Developing themethod of server controlled outcomes management and variance analysis [J]. Electron. Notes Theor. Comput. Sci. 225:221-237, 2009.
2. Wakamiya, S., and Yamauchi, K., A new approach to systematization of the management of paper-based clinical pathways. Comput. Meth. Programs Biomed. 82:169-176, 2006.
3. Du, G., Jiang, Z. B., Diao, X. D., Ye, Y., and Yao Y., Modeling, variation monitoring, analyzing, reasoning for intelligently reconfigurable clinical pathway. Proceedings of the IEEE International Conference on Service Operations, Logistics and Informatics, pp. 85-90, Chicago, IL, USA, 2009.
4. Agrawal, R., Imielinski, T., and Swami, A., Database mining: A performance perspective. IEEE Trans. Knowl. Data Eng. 5:914-925, 1993.
5. Berry, M. J. A., and Linoff, G. S., Data mining techniques. Wiley, New York, 1997.
6. Kim, Y. S., and Street, W. N., An intelligent system for customer targeting: A data mining approach. Decis. Support Syst. 37:215-228, 2004.
7. Haughton, D., and Oulabi, S., Direct marketing modeling with CART and CHAID. J. Direct Mark. 11(4):42-52, 1997.
8. Cheung, K.-W., Kwok, J. T., Law, M. H., and Tsui, K. C., Mining customer product rating for personalized marketing. Decis. Support Syst. 35:231-243, 2003.
9. Kim, Y. S., Street, W. N., and Menczer, F., Optimal ensemble construction via meta-evolutionary ensembles. Expert Syst. Appl. 30:705-714, 2006. (Pubitemid 43267430)
10. Tsai, Y. C., Cheng, C. H., and Chang, J. R., Entropy-based fuzzy rough classification approach for extracting classification rules. Expert Syst. Appl. 31(2):436-443, 2006.
11. Chou, S.-M., Lee, T.-S., Shao, Y. E., and Chen, I.-F., Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst. Appl. 27:133-142, 2004.
12. Andrews, R., Diederich, J., and Tickle, A. B., Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl.-Based Syst. 8:373-389, 1995.
13. Fu, L., Rule learning by searching on adapted nets. In: Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, pp. 590-595, 1991.
14. Towell, G., and Shavlik, J. W., The extraction of refined rules from knowledge-based neural networks. Mach. Learn. 13:71-101, 1993.
15. Freitas, A. A., Data mining and knowledge discovery with evolutionary algorithms. 1 edition. Publisher: Springer, 2002.
16. Lin, S.-W., Shiue, Y.-R., Chen, S.-C., and Cheng, H.-M., Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks. Expert Syst. Appl. 36:11543-11551, 2009.
17. Huang, C. L., and Wang, C. J., A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31:231-240, 2006.
18. Kennedy, J., and Eberhart, R. C., Particle swarm optimization. [C]//IEEE International Conference on Neural Networks. Piscataway, 1942-1948, 1995.
19. Sousa, T., Silva, A., and Neves, A., Particle swarm based data mining algorithms for classification tasks. Parallel Comput. 30(5-6):767-783, 2004.
20. Shi, Y., and Eberhart, R. C., Empirical study of particle swarm Optimization. Proc. IEEE Int. Conf. Evol. Comput. 3:101-106, 1999.
21. Ratnaweera, A., Halgamuge, S. K., and Watoson, H. C., Selforganizing hierarchical particle swarm optimizer with timevarying acceleration coefficient. IEEE Trans. Evol. Comput. 8 (3):240-255, 2004.
22. Lovbjerg, M., Rasmussen, T. K., and Krink, T., Hybrid particle swarm optimiser with breeding and sub-populations. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). SanFrancisco, CA, July 2001.
23. Xie, X. F., Zhang, W. J., et al., Optimizing semiconductor devices by self-organizing particle swarm. Congress on Evolutionary Computation, Oregon, USA, 2017-2022, 2004.
24. He, R., Wang, Y., et al., An improved particle swarm optimization based on self-adaptive escape velocity. J. Software 16(12):2036-2044, 2005.
25. Van den Bergh, F., and Engelbrecht, A. P., A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8(3):225-239, 2004.
26. Higashi, N., and Iba, H., Particle swarm optimization with Gaussian mutation. Proceedings of the IEEE Swarm Intelligence Symp. Indianapolis: IEEE Inc, 72-79, 2003.
27. Kennedy, J., and Eberhart, R. C., A discrete binary version of the particle swarm algorithm, systems, man, and cybernetics. Comput. Cybern. Simul. IEEE Int. Conf. 5(12-15):4104-4108, 1997.
28. Chen, G., and Yu, J., Two sub-swarms particle swarm optimization algorithm. Lect. Notes Comput. Sci. 3612:515-524, 2005. (Pubitemid 41449614)
29. Shi, Y., and Eberhart, R. C., Parameter selection in particle swarm optimization. Lect. Notes Comput. Sci. 1447/1998:591-600, 1997.
30. Freitas, A. A., On objective measures of rule surprisingness, [C]//Proc of the 2nd European Symp on Principles of Data Mining and Knowledge Discovery, 1-9, 1998.
31. Yeh, W.-C., Chang, W.-W., and Chuang, Y. Y., A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst. Appl. 36:8204-8211, 2009.
32. Chen, S.-M., and Yu, C.-H., A new method to generate fuzzy rules from training instances for handling classification problems. Cybern. Syst. 34(3):217-232, 2003.
33. Wu, T. P., and Chen, S. M., A new method for constructing membership functions and fuzzy rules from training examples. IEEE Trans. Syst. Man Cybern. Part B 29(1):25-40, 1999.
34. Hong, T. P., and Lee, C. Y., Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst. 84 (1):34-37, 1996.
35. Shi, Y., and Eberhart, R., A modified paricle swarm optimizer [A]. In: Proc.of IEEE Int. Conf. On Evolutionary Computation[C]. Anchorage, 69-73, 1998.
36. Chen, T.-C., and Hsu, T.-C., A GAs based approach for mining breast cancer pattern. Expert Syst. Appl. 30:674-681, 2006. (Pubitemid 43267427)