Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Applied Soft Computing Journal

Volumn 35, Issue , 2015, Pages 513-524

  Alweshah, Mohammed ^a   Abdullah, Salwani ^b  

^a AL BALQA APPLIED UNIVERSITY   (Jordan)

^b UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

Classification problems; Firefly algorithm; L vy flight; Probabilistic neural networks; Simulated annealing

Indexed keywords

ALGORITHMS; BIOLUMINESCENCE; NEURAL NETWORKS; OPTIMIZATION; PROBLEM SOLVING; SIMULATED ANNEALING;

BENCHMARK DATASETS; BETTER PERFORMANCE; CLASSIFICATION ACCURACY; CONVERGENCE SPEED; FIREFLY ALGORITHMS; HIGH-QUALITY SOLUTIONS; PROBABILISTIC NEURAL NETWORKS; SEARCH SPACES;

DATA MINING;

EID: 84937559609     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.06.018     Document Type: Article

References (63)

1. G.P. Zhang Neural networks for classification: a survey IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 30 2002 451 462
2. Y.J. Lee, and O.L. Mangasarian SSVM: a smooth support vector machine for classification Comput. Optim. Appl. 20 2001 5 22
3. N. Friedman, and et al. Bayesian network classifiers Mach. Learn. 29 1997 131 163
4. A. Wai-Ho, and K.C.C. Chan Classification with degree of membership: a fuzzy approach, presented at the Proceedings in International Conference on Data Mining, California, USA 2001
5. E.M. Azoff Neural Network Time Series Forecasting of Financial Markets 1994 John Wiley & Sons, Inc.
6. J.K. Han, and M. Kamber Data Mining: Concepts and Techniques 2008 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
7. M. Alweshah Firefly algorithm with artificial neural network for time series problems Res. J. Appl. Sci. Eng. Technol. 7 2014 3978 3982
8. Y. Zhang, and L. Wu Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network Expert Syst. Appl. 36 2009 8849 8854
9. M.N. French, and et al. Rainfall forecasting in space and time using a neural network J. Hydrol. 137 1992 1 31
10. B.D. Ripley Pattern Recognition and Neural Networks 2008 Cambridge Univ. Press
11. D.F. Specht Probabilistic neural networks Neural Netw. 3 1990 109 118
12. J. Han, and M. Kamber Data Mining: Concepts and Techniques 2006 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
13. W.P. Sweeney Jr., and et al. Classification of chromosomes using a probabilistic neural network Cytometry 16 1994 17 24
14. S. Chalup, and F. Maire A study on hill climbing algorithms for neural network training Proc. Congress on Evolutionary Computation 1999 2014 2021
15. N.K. Treadgold, and T.D. Gedeon Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm Neural Netw. 9 1998 662 668
16. E. Alba, and J. Chicano Training neural networks with GA hybrid algorithms, presented at the Proceedings of GECCO'04, Seattle, Washington 2004
17. P. Malinak, and R. Jaksa Simultaneous gradient and evolutionary neural network weights adaptation methods, presented at the IEEE Congress on Evolutionary Computation (CEC) 2007
18. F. Zhao, and et al. Application of an improved particle swarm optimization algorithm for neural network training, presented at the Conference on Neural Networks and Brain (ICNN&B'05) 2005
19. M.G.H. Omran Using opposition-based learning with particle swarm optimization and barebones differential evolution Particle Swarm Optimization vol. 23 2009 InTech Education and Publishing 343 384
20. K. Socha, and C. Blum An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training Neural Comput. Appl. 16 2007 235 247
21. C. Blum, and K. Socha Training feed-forward neural networks with ant colony optimization: an application to pattern classification Fifth International Conference on Hybrid Intelligent Systems (HIS'05) 2005 233 238
22. X. Chen, and et al. A novel hybrid evolutionary algorithm based on PSO and AFSA for feedforward neural network training, presented at the IEEE 4th International Conference on Wireless Communications, Networking and Mobile Computing 2008
23. D.J. Montana, and L. Davis Training feedforward neural networks using genetic algorithms, presented at the International Joint Conference on Artificial Intelligence, USA 1989
24. A. Slowik, and M. Bialko Training of artificial neural networks using differential evolution algorithm, presented at the Conference on Human System Interactions, Amsterdam 2008
25. Y.D. Zhang, and L. Wu Weights optimization of neural network via improved BCO approach Prog. Electromagn. Res. 83 2008 185 198
26. X.J. Wang, and et al. Electromagnetism-like mechanism based algorithm for neural network training Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence 2008 40 45
27. A. Kattan, and R. Abdullah A parallel & distributed implementation of the harmony search based supervised training of artificial neural networks, presented at the ISMS 2011
28. A. Kattan, and R. Abdullah An enhanced parallel & distributed implementation of the harmony search based supervised training of artificial neural networks, presented at the CICSyN 2011
29. S. Kulluk, and et al. Self-adaptive global best harmony search algorithm for training neural networks Procedia Comput. Sci. 3 2011 282 286
30. A. Kattan, and et al. Harmony search based supervised training of artificial neural networks, presented at the ISMS'10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation, Washington, DC, USA 2010
31. C. Blum, and A. Roli Metaheuristics in combinatorial optimization: overview and conceptual comparison ACM Comput. Surv. 35 2003 268 308
32. M. Dorigo, and C. Blum Ant colony optimization theory: a survey Theor. Comput. Sci. 344 2005 243 278
33. X.S. Yang Nature-Inspired Metaheuristic Algorithms 2008 Luniver Press
34. X.S. Yang Firefly algorithms for multimodal optimization Stoch. Algorithms: Found. Appl. 2009 169 178
35. T. Apostolopoulos, and A. Vlachos Application of the firefly algorithm for solving the economic emissions load dispatch problem Int. J. Comb. 2011 2011 1 23
36. X.-S. Yang Engineering Optimization: An Introduction with Metaheuristic Applications 2010 Wiley
37. S. Łukasik, and S. Żak Firefly algorithm for continuous constrained optimization tasks Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems 2009 97 106
38. X.S. Yang, and S. Deb Eagle strategy using lévy walk and firefly algorithms for stochastic optimization Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) 2010 101 111
39. M.K. Sayadi, and et al. A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems Int. J. Ind. Eng. Comput. 1 2010 1 10
40. A.H. Gandomi, and et al. Mixed variable structural optimization using firefly algorithm Comput. Struct. 2011 2325 2336
41. H.M.J. Hung, and et al. The behavior of the P-value when the alternative hypothesis is true Biometrics 53 1997 11 22
42. M. Paliwal, and U.A. Kumar Neural networks and statistical techniques: a review of applications Expert Syst. Appl. 36 2009 2 17
43. S. Bertrand, and et al. Scale-invariant movements of fishermen: the same foraging strategy as natural predators Ecol. Appl. 17 2007 331 337
44. X.S. Yang, and S. Deb Cuckoo Search via Lévy flights 2009 IEEE 210 214
45. H. Pham, and E. Triantaphyllou A meta-heuristic approach for improving the accuracy in some classification algorithms Comput. Oper. Res. 38 1 2011 174 189
46. L. Rutkowski, and K. Cpalka Flexible neuro-fuzzy systems Neural Netw. 14 2003 554 574
47. W. Leon IV Enhancing Pattern Classification with Relational Fuzzy Neural Networks and Square BK-products (PhD dissertation in computer science) 2006 Springer FL, USA
48. G. Lei, and et al. A novel classification algorithm based on fuzzy kernel multiple hyperspheres, presented at the Proceedings of the fourth international conference on fuzzy systems and knowledge discovery, FSKD Haikou, Hainan, China 2007
49. V.A. Kecman, and I. T. Hadzic LP and QP based learning from empirical data, presented at the Proceedings of International Joint Conference on Neural Networks (IJCNN 2001), Washington, DC 2001
50. G.M. Fung, and O.L. Mangasarian Multicategory proximal support vector machine classifiers Mach. Learn. 59 2005 77 97
51. C.L. Huang, and et al. Credit scoring with a data mining approach based on support vector machines Expert Syst. Appl. 33 2007 847 856
52. N. Segata, and E. Blanzieri Empirical Assessment of Classification Accuracy of Local SVM 2008
53. F. Zarndt A Comprehensive Case Study: An Examination of Machine Learning and Connectionist Algorithms (PhD thesis) 1995 Dept. Comput. Sci., Brigham Young Univ.
54. M. Ene Neural network-based approach to discriminate healthy people from those with Parkinson's disease Ann. Univ. Craiova-Math. Comput. Sci. Ser. 35 2008 112 116
55. L.A. Kurgan, and et al. Knowledge discovery approach to automated cardiac SPECT diagnosis Artif. Intell. Med. 23 2001 149 169
56. F. Al-Obeidat, and et al. Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization, presented at the Canadian Conference on AI 2010
57. K. Polat, and et al. Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism Expert Syst. Appl. 32 2007 172 183
58. R.J. Mooney Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning 1996 The Computing Research Repository (CoRR) 82 91
59. H.N.A. Pham, and E. Triantaphyllou A meta-heuristic approach for improving the accuracy in some classification algorithms Comput. Oper. Res. 38 2011 174 189
60. H. Salar, and F. Farrokhi Improving genetic algorithm performance in multi-classification using simplex method, presented at the First International Conference on Integrated Intelligent Computing (ICIIC) 2010
61. X.S. Yang Nature-inspired Metaheuristic Algorithms 2011(2008) Luniver Press
62. E.-G. Talbi Metaheuristics: from Design to Implementation 2009 Wiley Online Library
63. F. Ancona, and et al. Implementing probabilistic neural networks Neural Comput. Appl. 5 1997 152 159