Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems

Information Sciences - Tập 239 - Trang 165-190 - 2013
Sultan Noman Qasem1,2, Siti Mariyam Shamsuddin3, Siti Zaiton Mohd Hashim3, Maslina Darus4, Eiman Tamah Al-Shammari5
1Computer Science Department, College of Computer and Information Sciences, Al-Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
2Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen
3Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
4School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia
5Information Science Department, College of Computing Sciences and Engineering, Kuwait University, Kuwait

Tóm tắt

Từ khóa


Tài liệu tham khảo

Abbass, 2001, A memetic Pareto evolutionary approach to artificial neural networks, Australian Joint Conference on Artificial Intelligence, 1

Abbass, 2002, An evolutionary artificial neural networks approach for breast cancer diagnosis, Artificial Intelligence in Medicine, 25, 265, 10.1016/S0933-3657(02)00028-3

Abbass, 2003, Speed up backpropagation using multi-objective evolutionary algorithms, Neural Computation, 15, 2705, 10.1162/089976603322385126

Abbass, 2003, Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization, IEEE Congress on Evolutionary Computation, 3, 2074

H.A. Abbass, R. Sarker, Simultaneous evolution of architectures and connection weights in ANNs, in: Proceedings of Artificial Neural Networks and Expert System Conference, 2001 pp. 16–21.

A. Asuncion, D. Newman, UCI machine learning repository, 2007. <http://www.ics.uci.edu/~mlearn/MLRepository.html>.

Y. Bai, L. Zhang, Genetic algorithm based self-growing training for RBF neural networks, in: Proceedings of International Joint Conference on Neural Networks, 2002, pp. 840–845.

Bishop, 1995

Broomhead, 1988, Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 321

Caballero, 2010, Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks, IEEE Transactions on Neural Networks, 21, 750, 10.1109/TNN.2010.2041468

Cai, 2010, A multi-objective simultaneous learning framework for clustering and classification, IEEE Transactions on Neural Networks, 21, 185, 10.1109/TNN.2009.2034741

Chakraborty, 2011, On convergence of the multi-objective particle swarm optimizers, Information Sciences, 181, 1411, 10.1016/j.ins.2010.11.036

Chan, 2011, Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm, Information Sciences, 181, 1623, 10.1016/j.ins.2011.01.006

Chawla, 2002, SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321, 10.1613/jair.953

Clerc, 2002, The particle swarm – explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6, 58, 10.1109/4235.985692

Coello, 2000, An updated survey of GA-based multi-objective optimization techniques, ACM Computer Survey, 32, 109, 10.1145/358923.358929

Coello, 2002, MOPSO: a proposal for multiple objective particle swarm optimization, 1051

Coello, 2004, Handling multi-objectives with particle swarm optimization, IEEE Transactions on Evolutionary Computation, 8, 256, 10.1109/TEVC.2004.826067

Coello, 2002

Deb, 1995, Simulated binary crossover for continuous search space, Complex System, 9, 115

Deb, 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017

Delgado, 2008, Multiobjective hybrid optimization and training of recurrent neural networks, IEEE Transactions on Systems, Man and Cybernetics—Part B, 38, 381, 10.1109/TSMCB.2007.912937

Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7, 1

Fernandez, 2011, Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: the extended NOSGA algorithm, Information Sciences, 181, 44, 10.1016/j.ins.2010.09.007

P.M. Ferreira, A.E. Ruano, C.M. Fonseca, Evolutionary multi-objective design of radial basis function networks for greenhouse environmental control, in: Proceedings of the 16th IFAC World Congress, 2005.

Fieldsend, 2005, Pareto evolutionary neural networks, IEEE Transaction Neural Networks, 16, 338, 10.1109/TNN.2004.841794

Garcı́a-Perdrajas, 2002, Multi-objective cooperative coevolution of artificial neural networks, Neural Networks, 15, 1259, 10.1016/S0893-6080(02)00095-3

N. Garcı́a-Perdrajas, E. Sanz-Tapia, D. Ortiz-Boyer, C. Hervás-Martı́neza, Introducing multi-objective optimization in cooperative coevolution of neural networks, in: Proceedings of 6th International Work-Conference of Artificial Natural Neural Network, 2001, pp. 645–652.

Goh, 2008, Hybrid multi-objective evolutionary design for artificial neural networks, IEEE Transactions on Neural Networks, 19, 1531, 10.1109/TNN.2008.2000444

González, 2003, Multi-objective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation, IEEE Transaction Neural Networks, 14, 1478, 10.1109/TNN.2003.820657

González, 2001, vol. 2084

L. Graning, Y. Jin, B. Sendhoff, Generalization improvement in multi-objective learning, in: Proceedings of the International Joint Conference on Neural Networks, 2006, pp. 4839–4846.

Han, 2011, An efficient self-organizing RBF neural network for water quality prediction, Neural Networks, 24, 717, 10.1016/j.neunet.2011.04.006

Hervás, 2008, Multilogistic regression by evolutionary neural network as a classification tool to discriminate highly overlapping signals: Qualitative investigation of volatile organic compounds in polluted waters by using headspacemass spectrometric analysis, Chemometrics Intelligent Laboratory Systems, 92, 179, 10.1016/j.chemolab.2008.03.005

Hsu, 2002, A comparison of methods for multi-class support vector machines, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427

Jin, 2004, Neural network regularization and ensembling using multi-objective evolutionary algorithms, IEEE Congress on Evolutionary Computation, 1

Y. Jin, B. Sendhoff, Alleviating catastrophic forgetting via multi-objective learning, in: Proceeding International Joint Conference Neural Network, 2006, pp. 3335–3342.

Jin, 2008, Pareto-based multiobjective machine learning: an overview and case studies, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 38, 397, 10.1109/TSMCC.2008.919172

Jin, 2005

J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.

J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, in: Proceedings of the 1997 Conference on Systems, Man, and Cybernetics, IEEE Service Center, Piscataway, NJ, 1997, pp. 4104–4109.

Kokshenev, 2008, A multi-objective approach to RBF network learning, Neurocomputing, 71, 1203, 10.1016/j.neucom.2007.11.021

Kokshenev, 2010, An efficient multi-objective learning algorithm for RBF neural network, Neurocomputing, 73, 2799, 10.1016/j.neucom.2010.06.022

Kondo, 2006, Pattern classification by evolutionary RBF networks ensemble based on multi-objective optimization, International Joint Conference on Neural Networks, 2919

N. Kondo, T. Hatanaka, K. Uosaki, Non linear dynamic system identification based on multi-objectively selected RBF networks, in: Proceedings of the IEEE Symposium on Computational Intelligence in Multi-criteria Decision Making, 2007, pp. 112–127.

E. Lacerda, A. Carvallho, T. Ludermir, Evolutionary optimization of RBF networks, in: Proceedings of the 6th Brazilian Symposium Neural Networks, 2000, pp. 219–224.

Lefort, 2006, vol. 3871

Leonard, 1991, Radial basis function networks for classifying process faults, Control Systems Magazine, 11, 31, 10.1109/37.75576

Liu, 1999, Multi-objective criteria for neural networks structure selection and identification of nonlinear systems using genetic algorithms, Proceedings Institute of Electrical Engineering – Control Theory and Applications, 146, 373, 10.1049/ip-cta:19990501

Qasem, 2011, Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis, Applied Soft Computing, 11, 1427, 10.1016/j.asoc.2010.04.014

C.R. Raquel, P.C. Naval, An effective use of crowding distance in multi-objective particle swarm optimization, in: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, 2005, pp. 257–264.

Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: IEEE Conference on Evolutionary Computation, 1998, pp. 69–73.

Teixeira, 2000, Improving generalization of MLPs with multi-objective optimization, Neurocomputing, 35, 189, 10.1016/S0925-2312(00)00327-1

Vakil-Baghmishah, 2004, Training RBF networks with selective backpropagation, Nuerocomputing, 62, 39, 10.1016/j.neucom.2003.11.011

Vapnik, 1998

Wang, 2012, A memetic particle swarm optimization algorithm for multimodal optimization problems, Information Sciences, 197, 38, 10.1016/j.ins.2012.02.016

Wang, 2009, Particle swarm optimization with preference order ranking for multi-objective optimization, Information Sciences, 179, 1944, 10.1016/j.ins.2009.01.005

Z. Wenbo, H. De-Shuang, Y. Ge, The structure optimization of radial basis probabilistic neural networks based on genetic algorithms, in: Proceedings of International Joint Conference on Neural Networks, 2002, pp. 1086–1091.

J. Weston, C. Watkins, Multi-class support vector machines, in: M. Verleysen, (Ed.), Presented at the Proc. ESANN99, Brussels, Belgium, 1999.

D.L. Whitley, V.S. Gordon, K.E. Mathias, Lamarckian evolution, the baldwing effect and function optimization, in: Proceedings of the International Conference on Evolutionary Computation, 1994, pp. 6–15.

Yao, 1999, Evolving artificial neural networks, Proceedings IEEE, 87, 1423, 10.1109/5.784219

Yen, 2006, vol. 16

Zhang, 2012, A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch, Information Sciences, 192, 213, 10.1016/j.ins.2011.06.004