Negative correlation in incremental learning

Natural Computing

Volumn 8, Issue 2, 2009, Pages 289-320

  Minku, Fernanda Li ^a   Inoue, Hirotaka ^a,b   Yao, Xin ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b KURE NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

Author keywords

Classification; Incremental learning; Multi layer perceptrons; Negative correlation learning; Neural network ensembles; Self generating neural tree; Self organising neural grove

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION ANALYSIS; DATA BASE; FUZZY SYSTEM; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; PROBLEM SOLVING;

EID: 67349147665     PISSN: 15677818     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11047-007-9063-7     Document Type: Article

References (37)

1. Adamczak R, Duch W, Jankowski N (1997) New developments in the feature space mapping model. In: Proceedings of the third conference on neural networks and their applications, Kule, Poland, pp 65-70
2. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123-140
3. Breiman L (2001) Random forests. Mach Learn 45(1):5-32
4. Brown G (2004) Diversity in neural network ensembles. Ph.D. Thesis, School of Computer Science, The University of Birmingham, Birmingham, UK. URL: Http://www.cs.man.ac.uk/~gbrown/research.php
5. Brown G, Wyatt JL, Tiño P (2005) Managing diversity in regression ensembles. J Mach Learn Res 6:1621-1650
6. Carpenter GA, Grossberg S, Reynolds JH (1991) ARTMAP: Supervied real-time learning and classification of nonstationary data by a self organizing neural network. Neural Networks 4(5):565-588
7. Carpenter GA, Grossberg S, Markuzon N, Reynolds JH (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Networks 3:698-713
8. Chandra A, Yao X (2006) Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69:686-700
9. Chandra A, Chen H, Yao X (2006) Trade-off between diversity and accuracy in ensemble generation. In: Jin Y (ed) Multi-objective machine learning. Springer-Verlag, pp 429-464
10. Dietterich TG (1997) Machine learning research: Four current directions. AI Mag 18:97-136
11. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895-1923
12. Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Mach Learn 40(2):1-22
13. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer-Verlag Berlin Heidelberg, New York
14. Freund Y, Schapire R (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comp Syst Sci 55(1):119-139
15. Inoue H, Narihisa H (2000) Improving generalization ability of self-generating neural networks through ensemble averaging. In: Proceedings of the fourth Pacific-Asia conference on knowledge discovery and data mining (LNAI 1805), Kyoto, Japan, pp 177-180
16. Inoue H, Narihisa H (2003) Effective pruning method for a multiple classifier system based on self-generating neural networks. In: Proceedings of the 2003 joint international conference (ICANN/ ICONIP '03-LNCS 2714), Istanbul, Turkey, pp 11-18
17. Inoue H, Narihisa H (2005) Self-organizing neural grove and its applications. In: Proceedings of the 2005 international joint conference on neural networks (IJCNN'05), Montreal, Canada, pp 1205-1210
18. Islam MM, Yao X, Murase K (2003) A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Networks 14(4):820-834
19. Kasabov N (2001) Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning. IEEE Trans Syst Man Cybernet - Part B: Cybernet 31(6):902-918
20. Kohonen T (1995) Self-organizing maps. Springer-Verlag, Berlin
21. Kuncheva L, Whitaker C (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181-207
22. Larose DT (2004) Discovering knowledge in data: An introduction to data mining. Wiley-Interscience
23. Liu Y, Yao X (1999a) Ensemble learning via negative correlation. Neural Networks 12:1399-1404
24. Liu Y, Yao X (1999b) Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Trans Syst Man Cybernet Part B - Cybernet 29(6):716-725
25. Newman D, Hettich S, Blake C, Merz C (1998) UCI repository of machine learning databases. URL: http://www.ics.uci.edu/~mlearn/ MLRepository.html
26. Polikar R, Udpa L, Udpa SS, Honavar V (2001) Learn++: An incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybernet - Part C: Appl Rev 31(4):497-508
27. Prechelt L (1994) PROBEN1 - a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, FakultSt fnr Informatik, UniversitSt Karlsruhe, Karlsruhe, Germany
28. Rätsch G, Onoda T, Müller K-R (2001) Soft margins for AdaBoost. Mach Learn 42(3):287-320
29. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel Distrib Process: Explor Microstruct Cogn I:318-362
30. Schapire R (1990) Strength of weak learning. Mach Learn 5:197-227
31. Schapire RE, Freund Y, Bartlett PL, Lee WS (1998) Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651-1686
32. Seipone T, Bullinaria J (2005) Evolving improved incremental learning schemes for neural network systems. In: Proceedings of the 2005 IEEE congress on evolutionary computing (CEC'2005), Piscataway, NJ, pp 273-280
33. Tang EK, Suganthan PN, Yao X (2006) An analysis of diversity measures. Mach Learn 62(1):247-271
34. Wang Z, Yao X, Xu Y (2004) An improved constructive neural network ensemble approach to medical diagnoses. In: Proceedings of the fifth international conference on intelligent data engineering and automated learning (IDEAL'04), Lecture Notes in Computer Science, vol 3177, Springer, Exeter, UK, pp 572-577
35. Wen WX, Jennings A, Liu H (1992) Learning a neural tree. In: Proceedings of the 1992 international joint conference on neural networks (IJCNN'92), vol 2, Beijing, China, pp 751-756
36. Witten IH, Frank E (2000) Data mining - pratical machine learning tools and techniques with Java implementations. Morgan Kaufmann Publishers, San Francisco
37. Zanchettin C, Minku FL, Ludermir TB (2005) Design of experiments in neuro-fuzzy systems. In: Proceedings of the 5th international conference on hybrid intelligent systems, HIS'2005, Rio de Janeiro, Brasil, pp 218-223