Probabilistic ensemble Fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms

Neural Computing and Applications

Volumn 26, Issue 2, 2015, Pages 263-276

  Loo, Chu Kiong ^a   Liew, Wei Shiung ^a   Seera, Manjeevan ^a   Lim, Einly ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Ensembles; Feature selection; Fuzzy ARTMAP; Parallel genetic algorithm; Plurality voting

Indexed keywords

FEATURE EXTRACTION; FUZZY NEURAL NETWORKS; HEURISTIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PARALLEL ARCHITECTURES;

ENSEMBLES; FUZZY ARTMAP; FUZZY-ARTMAP NEURAL NETWORK; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; PARALLEL GENETIC ALGORITHMS; PLURALITY VOTING; PROBABILISTIC ENSEMBLE; UCI MACHINE LEARNING REPOSITORY;

GENETIC ALGORITHMS;

EID: 84921699073     PISSN: 09410643     EISSN: 14333058     Source Type: Journal    
DOI: 10.1007/s00521-014-1632-y     Document Type: Article

References (46)

1. Carpenter GA, Grossberg S, Marzukon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5):698–713
2. Jin M, Li R, Xu Z, Zhao X (2014) Reliable fault diagnosis method using ensemble Fuzzy ARTMAP based on improved Bayesian belief method. Neurocomputing 133(10):309–316
3. Mokhtar M, Howe J (2013) Comparing the online learning capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for building energy management systems. Expert Syst Appl 40(15):6007–6018
4. Tran VT, AlThobiani F, Ball A, Choi BK (2013) An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP. Expert Syst Appl 40(13):5372–5384
5. Liang Z, Xu B, Chi Z, Feng D (2012) Intelligent characterization and evaluation of yarn surface appearance using saliency map analysis, wavelet transform and fuzzy ARTMAP neural network. Expert Syst Appl 39(4):4201–4212
6. Tan SC, Lim CP (2010) Evolutionary fuzzy artmap neural networks and their applications to fault detection and diagnosis. Neural Process Lett 31(3):219–242
7. Carpenter GA, Gaddam SC (2010) Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction. Neural Netw 23(3):435–451
8. Dagher I, Georgiopoulos M, Heileman GL, Bebis G (1999) An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance. IEEE Trans Neural Netw 10(4):768–778
9. Al-Daraiseh A, Georgiopoulos M, Anagnostopoulos G, Wu AS, Mollaghasemi M (2006) GFAM: a genetic algorithm optimization of fuzzy ARTMAP. In: Proceedings of the 2006 IEEE International Conference on Fuzzy Systems, pp 315–322
10. Granger E, Henniges P, Oliveira L, Sabourin R (2006) Particle swarm optimization of fuzzy ARTMAP parameters. In: Proceedings of the 2006 International Joint Conference on Neural Networks, pp 2060–2067
11. Palaniappan R, Eswaran C (2009) Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance. Appl Soft Comput 9(1):100–106
12. Cervantes L, Lee J, Lee J (2007) Agent-based approach to distributed ensemble learning of Fuzzy ARTMAP classifiers. In: Jo GS et al (eds) Agent and multi-agent systems: technologies and applications. Springer, Berlin, pp 805–814
13. de Medeiros Santos A, de Paula Canuto AM (2008) Using ARTMAP-based ensemble systems designed by three variants of boosting. In: Kurkova-Pohlova V et al (eds) Artificial neural networks—ICANN 2008. Springer, Berlin, pp 562–571
14. Williamson JR (1996) Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw 9(5):881–897
15. Gómez-Sánchez E, Dimitriadis YA, Cano-Izquierdo JM, López-Coronado J (2002) $$\mu$$μARTMAP: use of mutual information for category reduction in fuzzy ARTMAP. IEEE Trans Neural Netw 13(1):58–69
16. Kaylani A, Georgiopoulos M, Mollaghasemi M, Anagnostopoulos GC (2008) MO-GART: multiobjective genetic ART architectures. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp 1425–1432
17. Zhang Y, Ji H, Zhang W (2014) TPPFAM: use of threshold and posterior probability for category reduction in fuzzy ARTMAP. Neurocomputing 124:63–71
18. Fernández C, Valle C, Saravia F, Allende H (2012) Behavior analysis of neural network ensemble algorithm on a virtual machine cluster. Neural Comput Appl 21(3):535–542
19. Seera M, Lim CP, Nahavandi S, Loo CK (2014) Condition monitoring of induction motors: a review and an application of an ensemble of hybrid intelligent models. Expert Syst Appl 41(10):4891–4903
20. Seera M, Lim CP (2014) A hybrid intelligent system for medical data classification. Expert Syst Appl 41(5):2239–2249
21. Sesmero MP, Alonso-Weber JM, Gutiérrez G, Ledezma A, Sanchis A (2012) A new artificial neural network ensemble based on feature selection and class recoding. Neural Comput Appl 21(4):771–783
22. Hu JJ, Goodman ED (2002) The hierarchical fair-competition (HFC) model for parallel evolutionary algorithms. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, pp 49–54
23. Zenobi G, Cunningham P (2001) Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In: de Raedt L et al. (eds) Machine learning: ECML 2001, pp 576–587
24. Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. In: Liu H et al. (eds) Feature extraction, construction and selection, pp 117–136
25. Opitz DW (1999) Feature selection for ensembles. In: Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99), pp 379–384
26. Cunningham P (2000) Overfitting and diversity in classification ensembles based on feature selection. In: Computer Science Technical Report TCD-CS-2000-07, Trinity College Dublin, pp 1–8
27. Oliveira LS, Morita M, Sabourin R (2006) Feature selection for ensembles using the multi-objective optimization approach. In: Jin Y (ed) Multi-objective machine learning. Springer, Berlin, pp 49–74
28. Canuto AMP, Vale KMO, Feitosa A (2011) A reinforcement-based mechanism to select features for classifiers in ensemble systems. Int J Comput Inf Syst Ind Manag Appl 3:324–335
29. Lin X, Yacoub S, Burns J, Simske S (2003) Performance analysis of pattern classifier combination by plurality voting. Pattern Recognit Lett 24(12):1959–1969
30. Loo CK, Rao M (2005) Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP. IEEE Trans Knowl Data Eng 17(11):1589–1593
31. Radtke PV, Sabourin R, Wong T, et al. (2006) Classification system optimization with multi-objective genetic algorithms. In: Tenth International Workshop on Frontiers in Handwriting Recognition
32. Ishibuchi H, Yamamoto T (2003) Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers. In: Cantu-Paz E et al (eds) Genetic and evolutionary computation GECCO 2003. Springer, Berlin, pp 1077–1088
33. Mohamed MA, Hegazy AEF, Badr AA (2011) Evolutionary fuzzy ARTMAP approach for breast cancer diagnosis. Int J Comput Sci Netw Secur 11(4):77–84
34. Qasem SN, Shamsuddin SM, Zain AM (2012) Multi-objective hybrid evolutionary algorithms for radial basis function neural network design. Knowledge-Based Syst 27:475–497
35. Wu CH, Tzeng GH, Lin RH (2009) A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 36:4725–4735
36. Frank A, Asuncion A (2011) UCI machine learning repository. http://archive.ics.uci.edu/ml
37. Dzeroski S, Zenko B (2002) Is combining classifiers better than selecting the best one? In: International Workshop and Conference on Machine Learning, pp 123–130
38. Partalas I, Tsoumakas G, Katakis I, Vlahavas I (2006) Ensemble pruning using reinforcement learning. In: Advances in artificial intelligence, Springer, pp 301–310
39. Zhang Y, Burer S, Street WN (2006) Ensemble pruning via semi-definite programming. J Mach Learn Res 7:1315–1338
40. Ordonez FJ, Ledezma A, Sanchis A (2008) Genetic approach for optimizing ensembles of classifiers. In: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS) Conference, pp 89–94
41. Wang SJ, Mathew A, Chen Y, Xi LF, Ma L, Lee J (2009) Empirical analysis of support vector machine ensemble classifiers. Expert Syst Appl 36(3):6466–6476
42. Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1(6):80–83
43. Dos Santos EM, Sabourin R, Maupin P (2009) Overfitting cautious selection of classifier ensembles with genetic algorithms. Inf Fus 10(2):150–162
44. Lofstrom T, Johansson U, Bostrom H (2009) Ensemble member selection using multi-objective optimization. In: IEEE 2009 Symposium on Computational Intelligence and Data Mining, IEEE, pp 245–251
45. Connolly JF, Granger E, Sabourin R (2012) Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition. Pattern Recognit 45(7):2460–2477
46. Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evol Comput 4(4):380–387