Username   Password       Forgot your password?  Forgot your username? 


Negative Correlation Incremental Integration Classification Method for Underwater Target Recognition

Volume 14, Number 5, May 2018, pp. 1040-1049
DOI: 10.23940/ijpe.18.05.p23.10401049

Ming Hea,b, Nianbin Wanga, Hongbin Wanga, Ci Chua, and Songyan Zhongc

aCollege of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
bCollege of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China
cBeijing Institute of Computer Technology and Applications, Beijing, 100854, China

(Submitted on January 29, 2018; Revised on March 12, 2018; Accepted on April 24, 2018)


In this paper, an incremental learning algorithm based on negative correlation learning (NCL) is used as an identification classifier for underwater targets. Based on Selective negative incremental learning SNCL (Selective NCL) algorithm in the process of training, there are numbers of hidden layer nodes that are difficult to determine training time. Problems such as over fitting analysis arise. The algorithm combined with Bagging makes the difference between individual network further increase, and ensures the generalization performance of the whole. On the basis of this method, the use of the selective integration method based on clustering and a new proposed algorithm called SANCLBag, combined with the convolution of underwater target recognition neural network shows that the proposed integration approach can make the difference between individual network in the classification process further increase, and ensure the whole generalization performance. The model has higher identification accuracy, and can effectively solve the problem of incremental learning.


References: 28

      1. Bauer, Eric, Kohavi, Ron, "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants," Machine Learning, vol. 36, no. 1-2, pp. 105-139, 1999
      2. L. Breiman, "Bagging Predictors," Machine Learning, vol., no., pp., 1996
      3. J. X. Che, Y. L. Yang, "Stochastic Correlation Coefficient Ensembles for Variable Selection," Journal of Applied Statistics, vol. 44, no., pp. 1-22, 2017
      4. M. S. Cheon, S. Ahmed, F. Alkhayyal, "A Branch-Reduce-Cut Algorithm for the Global Optimization of Probabilistically Constrained Linear Programs," in Proceedings of Mathematical Programming, Ser B, pp. 617-634, 2006
      5. T. G. Dietterich, "Ensemble Methods in Machine Learning," Proc 1st International Workshop on Multiple Classifier Systems, 2000, vol. 1857, no. 1, pp. 1-15, 2000
      6. Y. Ding, "Negative Correlation Learning Based on Neural network," Computer Simulation, vol. 24, no. 6, pp. 142-145, 2007
      7. J. Friedman, T. Hastie, R. Tibshirani, "Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting," Annals of Statistics, vol. 28, no. 2, pp. 337-374, 2000
      8. Y. Fuji "Artificial Neural Network." Springer Publishing Company, Incorporated, 2016
      9. S. Hilbert "Correlation Coefficient." Springer International Publishing, 2017
      10. N. Kasabov, "Evolving Fuzzy Neural Networks," Algorithms, Applications and Biological Motivation, vol., no., pp., 1998
      11. N. Kasabov, "Evolving Fuzzy Neural Networks for Supervised/Unsupervised Online Knowledge-based Learning," IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, vol. 31, no. 6, pp. 902-918, 2001
      12. M. Korytkowski, L. Rutkowski, R. Scherer, "Fast Image Classification by Boosting Fuzzy Classifiers," Information Sciences, vol. 327, no. C, pp. 175-182, 2016
      13. G. Krempl, V. Lemaire, R. Polikar, B. Sick, D. Kottke, A. Calma, "Interactive Adaptive Learning," vol., no., pp., 2017
      14. L. I. Kuncheva "Ensemble Methods." John Wiley & Sons, Inc., 2014
      15. R. Q. Leo Bbeiman, "Bagging Predictors," vol., no., pp., 2010
      16. M. Lin, K. Tang, X. Yao, "Selective Negative Correlation Learning Algorithm for Incremental Learning," in Proceedings of IEEE International Joint Conference on Neural Networks, pp. 2525-2530, 2008
      17. Y. Liu, "Enforcing Negativity in Negative Correlation Learning," in Proceedings of IEEE International Conference on Information and Automation, pp. 1122-1125, 2017
      18. Y. Liu, X. Yao, "Ensemble Learning via Negative Correlation," Neural Networks, vol. 12, no. 10, pp. 1399-1404, 1999
      19. F. L. Minku, H. Inoue, X. Yao, "Negative Correlation in Incremental Learning," Natural Computing, vol. 8, no. 2, pp. 289-320, 2009
      20. A. Onwuegbuzie, L. Daniel, N. Leech, A. Onwuegbuzie, L. Daniel, N. Leech, A. Onwuegbuzie, L. Daniel, N. Leech, "Pearson Product-moment Correlation Coefficient," Covariance, vol., no., pp., 2012
      21. N. C. Oza, "Online Ensemble Learning," in Proceedings of Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, July 30 - August 3, 2000, Austin, Texas, USA, pp. 1109, 2000
      22. Polikar, Robi, "Ensemble Based Systems in Decision Making," IEEE Circuits & Systems Magazine, vol. 6, no. 3, pp. 21-45, 2006
      23. R. Polikar, L. Upda, S. S. Upda, V. Honavar, "Learn++: An Incremental Learning Algorithm for Supervised Neural Networks," Systems Man & Cybernetics Part C Applications & Reviews IEEE Transactions on, vol. 31, no. 4, pp. 497-508, 2001
      24. J. Sommars, J. Verschelde, "Pruning Algorithms for Pretropisms of Newton Polytopes," in Proceedings of International Workshop on Computer Algebra in Scientific Computing, pp. 489-503, 2016
      25. A. H. Tan, "Adaptive Resonance Associative Map," Neural Networks, vol. 8, no. 3, pp. 437-446, 1995
      26. K. Tang, M. Lin, F. L. Minku, X. Yao, "Selective Negative Correlation Learning Approach to Incremental Learning," Neurocomputing, vol. 72, no. 13–15, pp. 2796-2805, 2009
      27. I. Tolstikhin, S. Gelly, O. Bousquet, C. J. Simongabriel, B. Schölkopf, "AdaGAN: Boosting Generative Models," vol., no., pp., 2017
      28. N. C. Yeo, K. H. Lee, Y. V. Venkatesh, S. H. Ong, "Colour Image Segmentation Using the Self-organizing Map and Adaptive Resonance Theory," Image & Vision Computing, vol. 23, no. 12, pp. 1060-1079, 2005


          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

          Download this file (IJPE-2018-05-23.pdf)IJPE-2018-05-23.pdf[Negative Correlation Incremental Integration Classification Method for Underwater Target Recognition]668 Kb
          This site uses encryption for transmitting your passwords.