A distributed PSO-SVM hybrid system with feature selection and parameter optimization

Applied Soft Computing Journal

Volumn 8, Issue 4, 2008, Pages 1381-1391

  Huang, Cheng Lung ^a   Dun, Jian Fan ^b  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^b HUAFAN UNIVERSITY   (Taiwan)

Author keywords

Data mining; Distributed computing; Feature selection; Particle swarm optimization; Support vector machines; Web service

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SYSTEMS; DATA MINING; DECISION SUPPORT SYSTEMS; FACE RECOGNITION; INFORMATION MANAGEMENT; INFORMATION SERVICES; OPTIMIZATION; SEARCH ENGINES; STRUCTURAL OPTIMIZATION; SUPPORT VECTOR MACHINES; TECHNOLOGY;

APPLICATION SERVERS; CLASSIFICATION ACCURACIES; COMPUTATIONAL TIME; DATA MINING SYSTEMS; DISTRIBUTED ARCHITECTURES; DISTRIBUTED COMPUTING; FEATURE SELECTION; FEATURE SUBSET SELECTION; HETEROGENEOUS COMPUTING; HYBRID SYSTEMS; INPUT FEATURES; KERNEL PARAMETERS; PARAMETER OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; SVM MODEL; WEB SERVICE; WEB SERVICE TECHNOLOGIES;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 50149108380     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.10.007     Document Type: Article

References (36)

1. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer-Verlag, NY, USA
2. Zhang G.P. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern.-Part C: Appl. Rev. 30 4 (2000) 451-462
3. Huang C.-L., and Wang C.-J. A GA-based attribute selection and parameter optimization for support vector machine. Expert Syst. Appl. 31 2 (2006) 231-240
4. Fröhlich H., and Chapelle O. Feature selection for support vector machines by means of genetic algorithms. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence. Sacramento, CA, USA (2003) 142-148
5. Hsu C.W., and Lin C.J. A simple decomposition method for support vector machine. Mach. Learn. 46 1-3 (2002) 219-314
6. Kennedy J., and Eberhart R.C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ (1995) 1942-1948
7. Kennedy J., Eberhart R.C., and Shi Y. Swarm Intelligence (2001), Morgan Kaufmann Publishers Inc., San Francisco, CA
8. van den Bergh F., and Engelbrecht A.P. A study of particle swarm optimization particle trajectories. Inf. Sci. 176 8 (2006) 937-971
9. Jiang C.-W., and Etorre B. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Math. Comput. Simul. 68 1 (2005) 57-65
10. Du F., Shi W., Chen L., Deng Y., and Zhu Z. Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization. Pattern Recogn. Lett. 26 5 (2005) 597-603
11. Yin P.-Y., Yu S.-S., Wang P.-P., and Wang Y.-T. A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems. Comput. Stand. Interfaces 28 4 (2006) 441-450
12. Da Y., and Xiurun G. An improved PSO-based ANN with simulated annealing technique. Neurocomputing 63 (2005) 527-533
13. Chatterjee A., and Siarry P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Operations Res. 33 3 (2006) 859-871
14. Shi X.-H., Liang Y.-C., Lee H.-P., Lu C., and Wang L.-M. An improved GA and a novel PSO-GA-based hybrid algorithm. Inf. Process. Lett. 93 5 (2005) 255-261
15. Allahverdi A., and Al-Anzi F.S. A PSO and a tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput. Operations Res. 33 4 (2006) 1056-1080
16. Lian Z., Gu X., and Jiao B. A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan. Appl. Math. Comput. 175 1 (2006) 773-785
17. Mendes R., Kennedy J., and Neves J. The informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8 3 (2004) 204-210
18. Li S.-T., and Li T.-S. Interoperable web-based data mining system by java distributed object computing. Proceedings of the 34th Annual Hawaii International Conference on System Sciences (2001)
19. Kecman V. Learning and Soft Computing (2001), MIT Press, Cambridge, MA, USA
20. Scho{combining double acute accent}lkopf B., and Smola A.J. Statistical Learning and Kernel Methods (2000), MIT Press, Cambridge, MA, USA
21. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge, UK
22. Bottou L., Cortes C., Denker J., Drucker H., Guyon I., Jackel L., LeCun Y., Muller U., Sackinger E., Simard P., and Vapnik V. Comparison of classifier methods: a case study in handwriting digit recognition. International Conference on Pattern Recognition (1994), IEEE Computer Society Press 77-87
23. Knerr S., Personnaz L., and Dreyfus G. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman J. (Ed). Neurocomputing: Algorithms, Architectures and Application (1990), Springer-Verlag
24. KreBel U. Pairwise Classification and Support Vector Machines, Advances in Kernel Methods-Support Vector Learning (1999), MIT Press, Cambridge, MA, USA pp. 254-268
25. Shi Y., and Eberhart R.C. A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation. Piscataway, NJ (1998) 69-73
26. Eberhart R.C., and Shi Y. Particle swarm optimization: developments, application and resources. Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1 (2001) 81-86
27. Kennedy J., and Eberhart R.C. A discrete binary version of the particle swarm algorithm. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics. Piscataway, NJ (1997) 4104-4109
28. Agrafiotis D.K., and Cedeno W. Feature selection for structure-activity correlation using binary particle swarms. J. Med. Chem. 45 5 (2002) 1098-1107
29. C.W. Hsu, C.C. Chang, C.J. Lin, A practical guide to support vector classification, available at: http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf, 2003.
30. H.T. Lin, C.J. Lin, A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, available at: http://www.csie.ntu.edu.tw/∼cjlin/papers/tanh.pdf, 2003.
31. Punch W.F., Goodman E.D., Pei M., Lai C.-S., Hovland P., and Enbody R. Further research on feature selection and classification using genetic algorithms. In: Stephanie Forrest (Ed). Fifth International Conference on Genetic Algorithms. San Mateo, CA, Morgan Kaufmann (1993) 557-564
32. Salzberg S.L. On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min. Knowl. Discov. 1 (1997) 317-327
33. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, Software available at: http://www.csie.ntu.edu.tw/∼cjlin/libsvm, 2001.
34. Gorodetsky V., Karsaeyv O., and Samoilov V. Software tool for agent-based distributed data mining. International Conference on Integration of Knowledge Intensive Multi-agent Systems (KIMAS). Cambridge, MA (2003) 710-715
35. P.M. Murphy, D.W. Aha, UCI Repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, http://www.ics.uci.edu/∼mlearn/MLRepository.html, 2001.
36. Ong C.-S., Huang J.-J., and Tzeng G.-H. Building credit scoring models using genetic programming. Expert Syst. Appl. 29 1 (2005) 41-47