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A Survey on Ensemble Combination Schemes of Neural Network

by Varuna Tyagi, Anju Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 16
Year of Publication: 2014
Authors: Varuna Tyagi, Anju Mishra
10.5120/16679-6784

Varuna Tyagi, Anju Mishra . A Survey on Ensemble Combination Schemes of Neural Network. International Journal of Computer Applications. 95, 16 ( June 2014), 18-21. DOI=10.5120/16679-6784

@article{ 10.5120/16679-6784,
author = { Varuna Tyagi, Anju Mishra },
title = { A Survey on Ensemble Combination Schemes of Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 16 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number16/16679-6784/ },
doi = { 10.5120/16679-6784 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:35.093893+05:30
%A Varuna Tyagi
%A Anju Mishra
%T A Survey on Ensemble Combination Schemes of Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 16
%P 18-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Neural network ensembles are the most effective approach to improve the neural network system. The combination of neural networks can provide more accurate result than a single network. The simple averaging, weighted averaging, majority voting and ranking are commonly used combination strategies, and from these strategies each method has its limitations like for which application area particular is suited . This paper present a survey on different ensemble combination schemes as invented in literature.

References
  1. M. B. Eisen, P. O. Brown, DNA Arrays For Analysis Of Gene Expression, Methods Enzymol. 303 (1999) 179–205.
  2. C. A. Harrington, C. Rosenow, J. Retief, Monitoring Gene Expression Using DNA Microarrays, Curr. Opin. Microbiol. 3 (2000) 285–291.
  3. A. Ben-Dor, L. Bruhn, N. Friedman, I. Nachman, M. Schummer, N. Yakhini. 2000. Tissue Classification With Gene Expression Profiles, J. Comput. Biol. 7 pp. 559–584.
  4. S. Dudoit, J. Fridlyand, T. P. Speed. 2000 Comparison Of Discrimination Methods For The Classification Of Tumors Using Gene Expression Data, Technical Report 576, Department Of Statistics, University Of California, Berkeley.
  5. Shuang Yang, Antony Browne, And Philip D. Picton 2002. Multistage Neural Network Ensembles. LNCS 2364, pp. 91–97, Springer-Verlag Berlin Heidelberg.
  6. Krogh, A. , & Vedelsby, J. 1995. Neural Network Ensembles Cross Validation And Active Learning. In G. Tesauro, D. Touretzky, & T. Leen (Eds. ), Advances In Neural Information Processing Systems (Pp. 231–238). Cambridge, MA: MIT Press.
  7. Breiman, L. 1996. Bagging Predictors. Machine Learning, Vol. 24, pp. 123-140.
  8. Freund, Y. ,Schapire, R. E. 1997. A Decision-Theoretic Generalization Of On-Line Learning And An Application To Boosting. Journal Of Computer And System Sciences, Vol. 55, pp. 119-139.
  9. Zeng, X. , Martinez, T. R. 2000. Using A Neural Network To Approximate An Ensemble Of Classifiers. Neural Processing Letters, 12, pp. 225-237.
  10. Lean Yu, Shouyang Wang, Kin Keung Lai. 2008 Credit Risk Assessment with a Multistage Neural Network Ensemble Learning Approach, Expert Systems With Applications 34 pp. 1434–1444, Elsevier.
  11. Lee, D. S. , Srihari, S. N. 1993 Handprinted Digit Recognition: A Comparison Of Algorithms. Pre-Proc. 3RD International Workshop On Frontiers In Handwriting Recognition, Buffalo, USA, pp. 153-162.
  12. Wolpert, D. H. 1992 Stacked Generalization. Neural Networks, 5, pp. 241-259.
  13. Partridge, D. , Griffith, N. 1995 Strategies For Improving Neural Net Generalisation. Neural Computing And Applications, 3, 27-37.
  14. Kittler, J. 1998 Combining Classifiers: A Theoretical Framework. Pattern Analysis And Applications, 1, pp. 18-27.
  15. Breiman, L. 1996. Bagging Predictors. Machine Learning, 26, 123–140. Chen, M. C. , & Huang, S. H. (2003). Credit Scoring And Rejected Instances Reassigning Through Evolutionary Computation Techniques. Expert Systems With Applications, 24, pp. 433–441.
  16. Sharkey, A. J. C. 1996. On Combining Artificial Neural Nets. Connection Science, 8, 299–314.
  17. Yang, S. , & Browne, A. 2004. Neural Network Ensembles: Combining Multiple Models For Enhanced Performance Using A Multistage Approach. Expert Systems, 21, pp. 279–288.
  18. Raviv, Y. , & Intrator, N. 1996. Bootstrapping With Noise: An Effective Regularization Technique. Connection Science, 8, pp. 355–372.
  19. Tumer, K. , & Ghosh, J. 1996. Error Correlations And Error Reduction In Ensemble Classifiers. Connection Science, 8, pp. 385–404.
  20. Hornik, K. , Stinchocombe, M. , & White, H. 1989. Multilayer Feedforward Networks Are Universal Approximators. Neural Networks, 2, pp. 359–366.
  21. White, H. 1990. Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings. Neural Networks, 3, pp. 535–549.
  22. Yu, L. , Wang, S. Y. , & Lai, K. K. 2005. A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR And ANN For Foreign Exchange Rates. Computers And Operations Research, 32, pp. 2523–2541.
  23. Lai, K. K. , Yu, L. , Wang, S. Y. , & Zhou, L. G. 2006. Credit Risk Analysis Using A Reliability-Based Neural Network Ensemble Model. Lecture Notes In Computer Science, 4132, pp. 682–690. Huijuan Lu, Jinxiang Zhang, And Lei Zhang. 2006. Tissue Classification Using Gene Expression Data And Artificial Neural Network Ensembles, Pp. 792 – 800, Springer-Verlag Berlin Heidelberg. LNBI 4115.
  24. Schapire, R. E. 1990. The Strength Of Weak Learnability. Machine Learning, Vol. 5, pp. 197–227
  25. Larranaga, P. , Lozano, J. A. 2001 Estimation Of Distribution Algorithms: A New Tool For Evolutionary Computation. Kluwer Academic Publishers.
  26. Lars Kai Hansen, Peter Salamon. 1990. Neural Network Ensembles , IEEE Transactions On Pattern Analysis And Machine Intelligence, VOL. 12, NO. IO, Pp 993-1001.
  27. Kyung-Joong Kim, Sung-Bae Cho. 2006. Ensemble Classifiers Based On Correlation Analysis for DNA Microarray Classification, Elsevier. Neurocomputing 70 pp. 187–199.
Index Terms

Computer Science
Information Sciences

Keywords

Survey Ensemble

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