Have a personal or library account? Click to login
Using the one–versus–rest strategy with samples balancing to improve pairwise coupling classification Cover

Using the one–versus–rest strategy with samples balancing to improve pairwise coupling classification

Open Access
|Mar 2016

References

  1. Allwein, E., Schapire, R. and Singer, Y. (2001). Reducing multiclass to binary: A unifying approach for margin classifiers, Journal of Machine Learning Research1: 113–141.
  2. Beyan, C. and Fisher, R. (2015). Classifying imbalanced data sets using similarity based hierarchical decomposition, Pattern Recognition48(5): 1653–1672.10.1016/j.patcog.2014.10.032
  3. Breiman, L. (1996). Bagging predictors, Machine Learning24(2): 123–140.10.1007/BF00058655
  4. Cateni, S., Colla, V. and Vannucci, M. (2014). A method for resampling imbalanced datasets in binary classification tasks for real-world problems, Neurocomputing135: 32–41.10.1016/j.neucom.2013.05.059
  5. Chang, C. and Lin, C. (2001). LIBSVM: A library for support vector machines, http://www.csie.ntu.edu.tw/jlin/libsvm.
  6. Chawla, N., Bowyer, K., Hall, L. and Kegelmeyer, W.P. (2002). SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research16: 321–357.10.1613/jair.953
  7. Chmielnicki, W., Roterman-Konieczna, I. and Stąpor, K. (2012). An improved protein fold recognition with support vector machines, Expert Systems20(2): 200–211.
  8. Chmielnicki, W. and Stąpor, K. (2010). Protein fold recognition with combined SVM-RDA classifier, in M.G. Romay and E. Corchado (Eds.), Hybrid Artificial Intelligence Systems, Lecture Notes in Artificial Intelligence, Vol. 6076, Springer, Berlin, pp. 162–169.10.1007/978-3-642-13769-3_20
  9. Chmielnicki, W. and Stąpor, K. (2012). A hybrid discriminative/generative approach to protein fold recognition, Neurocomputing75(1): 194–198.10.1016/j.neucom.2011.04.033
  10. Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research7: 1–30.
  11. Dietterich, T. (1998). Approximate statistical tests for comparing supervised classification learning algorithms, Neural Computation10: 1895–1924.10.1162/0899766983000171979744903
  12. Dietterich, T.G. and Bakiri, G. (1995). Solving multiclass problems via error-correcting output codes, Journal of Artificial Intelligence Research2: 263–286.10.1613/jair.105
  13. Ding, C. and Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks, Bioinformatics17(4): 349–358.10.1093/bioinformatics/17.4.34911301304
  14. Fei, B. and Liu, J. (2006). Binary tree of SVM: A new fast multiclass training and classification algorithm, IEEE Transactions on Neural Networks17(3): 696–704.10.1109/TNN.2006.87234316722173
  15. Friedman, J. (1996). Another approach to polychotomous classification, Technical report, Stanford University, Stanford, CA.
  16. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F. (2011). An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recognition44(8): 1761–1776.10.1016/j.patcog.2011.01.017
  17. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F. (2013). Dynamic classifier selection for one-vs-one strategy: Avoiding non-competent classifiers, Pattern Recognition46(12): 3412–3424.10.1016/j.patcog.2013.04.018
  18. Glomb, P., Romaszewski, M., Opozda, S. and Sochan, A. (2011). Choosing and modeling hand gesture database for natural user interface, Proceedings of the 9th International Conference on Gesture and Sign Language in Human-Computer Interaction and Embodied Communication, Athens, Greece, pp. 24–35.
  19. Hastie, T. and Tibshirani, R. (1998). Classification by pairwise coupling, The Annals of Statistics26(1): 451–471.10.1214/aos/1028144844
  20. He, H. and Garcia, E. (2009). Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering21(9): 1263–1284.10.1109/TKDE.2008.239
  21. Hollander, M. and Wolfe, D. (1973). Nonparametric Statistical Methods, John Wiley and Sons, New York, NY.
  22. Iman, R. and Davenport, J. (1980). Approximations of the critical region of the Friedman statistics, Communications in Statistics—Theory and Methods9(6): 571–595.10.1080/03610928008827904
  23. Kahsay, L., Schwenker, F. and Palm, G. (2005). Comparison of multiclass SVM decomposition schemes for visual object recognition, in W. Kropatsch et al. (Eds.), Pattern Recognition, Lecture Notes in Computer Science, Vol. 3663, Springer, Berlin, pp. 334–341.10.1007/11550518_42
  24. Kijsirikul, B. and Ussivakul, N. (2002). Multiclass support vector machines using adaptive directed acyclic graph, Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI, USA, pp. 980–985.
  25. Krawczyk, B., Wozniak, M. and Cyganek, B. (2014). Clusterting-based ensembles for one-class classification, Information Sciences264: 182–195.10.1016/j.ins.2013.12.019
  26. Krzysko, M. and Wolynski, W. (2009). New variants of pairwise classification, European Journal of Operational Research199(2): 512–519.10.1016/j.ejor.2008.11.009
  27. LeCun, Y., Cortes, C. and Burges, Ch.J.C. (2014). The MNIST database of handwritten digits, http://yann.lecun.com/exdb/mnist/.
  28. Liu, C. and Fujisava, H. (2005). Classification and learning for character recognition: Comparison of methods and remaining problems, International Workshop on Neural Networks and Learning in Document Analysis and Recognition, Seoul, Korea, pp. 1–7.
  29. Liu, X., Wu, J. and Zhou, Z.H. (2008). Exploratory undersampling for class-imbalance learning, IEEE Transactions on Systems, Man and Cybernetics B39(2): 539–550.10.1109/TSMCB.2008.200785319095540
  30. Lorena, A. and Carvalho, A. (2010). Building binary-tree-based multiclass classifiers using separability measures, Neurocomputing73(16–18): 2837–2845.10.1016/j.neucom.2010.03.027
  31. Lorena, A., Carvalho, A. and Gama, J. (2008). A review on the combination of binary classifiers in multiclass problems, Artificial Intelligence Review30(1–4): 19–37.10.1007/s10462-009-9114-9
  32. Moreira, M. and Mayoraz, E. (1998). Improved pairwise coupling classification with correcting classifiers, Proceedings of the 10th European Conference on Machine Learning, ECML 1998, Chemnitz, Germany, pp. 160–171.
  33. Nadeau, C. and Bengio, Y. (2003). Inference for the generalization error, Advances in Neural Information Processing Systems52(3): 239–281.
  34. Nemenyi, P. (1963). Distribution-free Multiple Comparisons, Ph.D. thesis, Princeton University, Princeton, NJ.
  35. Ou, G. and Murphey, Y. (2006). Multi-class pattern classification using neural networks, Pattern Recognition40(1): 4–18.10.1016/j.patcog.2006.04.041
  36. Platt, J., Cristianini, N. and Shawe-Taylor, J. (2000). Large margin DAGs for multiclass classification, Neural Information Processing Systems, NIPS’99, Breckenridge, CO, USA, pp. 547–553.
  37. Saez, J.A., Galar, M., Luengo, J. and Herrera, F. (2012). A first study on decomposition strategies with data with class noise using decision trees, Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems, Salamanca, Spain, Part II, pp. 25–35.
  38. UCIMLR (2014). UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html.
  39. Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer, New York, NY.10.1007/978-1-4757-2440-0
  40. Vural, V. and Dy, J. (2004). A hierarchical method for multi-class support vector machines, Proceedings of the 21st International Conference on Machine Learning, St. Louis, MO, USA, pp. 831–838.
  41. Wilcoxon, F. (1945). Individual comparisons by ranking methods, Biometrics1(6): 80–83.10.2307/3001968
DOI: https://doi.org/10.1515/amcs-2016-0013 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 191 - 201
Submitted on: Nov 2, 2014
Published on: Mar 31, 2016
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Wiesław Chmielnicki, Katarzyna Stąpor, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.