Particle swarm and bayesian networks applied to attribute selection for protein functional classification

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, Companion Material

Volumn , Issue , 2007, Pages 2651-2658

  Correa, Elon S ^a   Freitas, Alex A ^a   Johnson, Colin G ^a  

^a UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Attribute selection; Bayesian networks; Bioinformatics; Data mining; Naive bayes classifier; Particle swarm

Indexed keywords

ALGORITHMS; BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); DATA MINING; OPTIMIZATION;

ATTRIBUTE SELECTION; NAIVE BAYES CLASSIFIER; PARTICLE SWARM; PREDICTIVE ACCURACY;

BAYESIAN NETWORKS;

EID: 34548065429     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1274000.1274081     Document Type: Conference Paper

References (25)

1. T. Blackwell and J. Branke. Multi-swarm optimization in dynamic environments. In Lecture Notes in Computer Science, volume 3005, pages 489-500. Springer-Verlag, 2004.
2. R. R. Bouckaert. Properties of Bayesian belief network learning algorithms. In I. R. L. de Mantaras and e. D. Poole, editors, Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pages 102-109, Seattle, WA, USA, 1994. Morgan Kaufmann.
3. D. M. Chickering, D. Geiger, and D. Heckerman. Learning Bayesian networks is NP-hard. Technical Report MSR-TR-94-17, Microsoft Research, November 1994.
4. E. S. Correa, A. A. Freitas, and C. G. Johnson. A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In M. K. et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2006, pages 35-42, Seattle, WA, USA, July 2006. ACM Press.
5. E. S. Correa, M. T. Steiner, A. A. Freitas, and C. Carnieri. Using a genetic algorithm for solving a capacity p-median problem. Numerical Algorithms, 35:373-388, 2004.
6. D. Filmore. It's a GPCR world. Modern drug discovery, 11(7):24-28, November 2004.
7. A. A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, October 2002.
8. N. Holden and A. A. Freitas. Hierarchical classification of g-protein-coupled receptors with a pso/aco algorithm. In Proc. IEEE Swarm Intelligence Symposium (SIS-06), pages 77-84. IEEE Press, June 2006.
9. S. Janson and M. Middendorf. A hierarchical particle swarm optimizer for dynamic optimization problems. In Evoworkshops 2004: 1st European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments, pages 513-524, Coimbra, Portugal, 2004. Springer-Verlag.
10. F. V. Jensen. Bayesian networks and decision graphs. Springer-Verlag, 1st edition, July 2001.
11. G. Kendall and Y. Su. A particle swarm optimisation approach in the construction of optimal risky portfolios. In Proceedings of the 23rd IASTED International Multi-Conference on Applied Informatics, pages 140-145, 2005. Artificial intelligence and applications.
12. J. Kennedy. Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress of Evolutionary Computation, pages 1931-1938, Piscataway, NJ, USA, 1999. IEEE Press.
13. J. Kennedy and R. C. Eberhart. A discrete binary version of the particle swarm algorithm. In Proceedings of the 1997 Conference on Systems, Man, and Cybernetics, pages 4104-4109, Piscataway, NJ, USA, 1997. IEEE.
14. J. Kennedy and R. C. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2001.
15. P. Larrañaga, R. Etxeberria, J. A. Lozano, B. Sierra, I. naki Inza, and J. M. Peña. A review of the cooperation between evolutionary computation and probabilistic models. In Second Symposium on Artificial Intelligence - CIMAF-1999, pages 314-324, Havana, Cuba, March 1999. Special Session on Distributions and Evolutionary Computation.
16. S. L. Lauritzen and D. J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistics Society 50, 2:157-224, 1988.
17. M. Løvbjerg and T. Krink. Extending particle swarm optimisers with self-organized criticality. In D. B. Fogel, M. A. El-Sharkawi, X. Yao, G. Greenwood, H. Iba, P. Marrow, and M. Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 1588-1593. IEEE Press, 2002.
18. T. M. Mitchell. Machine Learning. McGraw-Hill, August 1997.
19. G. L. Pappa, A. J. Baines, and A. A. Freitas. Predicting post-synaptic activity in proteins with data mining. Bioinformatics, 21(2):ii19-ii25, 2005.
20. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1st edition, September 1988.
21. J. M. Peña, J. A. Lozano, and P. Larrañaga. Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of bayesian networks. In Evolutionary Computation, volume 13, pages 43-66. MIT Press, January 2005.
22. R. Poli, C D. Chio, and W. B. Langdon. Exploring extended particle swarms: a genetic programming approach. In GECCO'05: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pages 169-176, New York, NY, USA, 2005. ACM Press.
23. Y. Shi and R. C. Eberhart. Parameter selection in particle swarm optimization. In EP'98: Proceedings of the 7th International Conference on Evolutionary Programming, pages 591-600, London, UK, 1998. Springer-Verlag.
24. M. M. Solomon. Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35(2):254-265, 1987.
25. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edition, 2005.