Skip to main content

Advertisement

Springer Nature Link
Log in

Problems selection under dynamic selection of the best base classifier in one versus one: PSEUDOVO

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of classes of the original problem. Different versions of OVO have been developed to try to solve some of its problems, such as DYNOVO, which dynamically tries to select the best classifier for each sub-problem. In this paper, a new extension that has been made for DYNOVO, called PSEUDOVO, is presented. This extension also tries to avoid the non-competent sub-problems. An empirical study has been carried out over several UCI data sets, as well as a new data set of musical pieces of well-known classical composers. Promising results have been obtained, from which can be concluded that the PSEUDOVO extension improves the performance of DYNOVO.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Anand R, Mehrotra K, Mohan CK, Ranka S (1995) Efficient classification for multiclass problems using modular neural networks. Trans Neural Netw 6(1):117–124

    Article  Google Scholar 

  2. Aridas CK, Alexandropoulos SAN, Kotsiantis SB, Vrahatis MN (2017) Random resampling in the one-versus-all strategy for handling multi-class problems. In: International conference on engineering applications of neural networks. Springer, pp 111–121

  3. Arruti A, Mendialdua I, Sierra B, Lazkano E, Jauregi E (2014) New one versus allone method: Nov@. Expert Syst Appl 41(14):6251–6260

    Article  Google Scholar 

  4. Bagheri MA, Gao Q, Escalera S (2012) Efficient pairwise classification using local cross off strategy. In: Kosseim L, Inkpen D (eds) Adv Artif Intell. Springer, Berlin, pp 25–36

    Chapter  Google Scholar 

  5. Cavalin PR, Sabourin R, Suen CY (2013) Dynamic selection approaches for multiple classifier systems. Neural Comput Appl 22(3):673–688

    Article  Google Scholar 

  6. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  7. Cohen WW (1995) Fast effective rule induction. In: Proceedings of the twelfth international conference on international conference on machine learning, ICML’95, pp. 115–123. Morgan Kaufmann Publishers Inc., San Francisco

  8. Cruz RM, Sabourin R, Cavalcanti GD (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41:195–216

    Article  Google Scholar 

  9. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  10. Deng H, Stathopoulos G, Suen CY (2009) Error-correcting output coding for the convolutional neural network for optical character recognition. In: 10th international conference on document analysis and recognition, ICDAR 2009, Barcelona, Spain, 26–29 July 2009, pp 581–585

  11. Dua D, Graff C (2019) UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA http://archive.ics.uci.edu/ml

  12. Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Int Res 2(1):263–286

    MATH  Google Scholar 

  13. Fei B, Liu J (2006) Binary tree of SVM: a new fast multiclass training and classification algorithm. IEEE Trans Neural Netw 17(3):696–704

    Article  MathSciNet  Google Scholar 

  14. Friedman JH (1996) Another approach to polychotomous classification. Department of Statistics, Stanford University, Tech. rep

  15. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163

    Article  MATH  Google Scholar 

  16. Fürnkranz J (2002) Round robin classification. J Mach Learn Res 2:721–747

    MathSciNet  MATH  Google Scholar 

  17. Galar M, Fernández A, Barrenechea E, Bustince H, 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 Recogn 44(8):1761–1776

    Article  Google Scholar 

  18. Galar M, Fernández A, Barrenechea E, Bustince H, Herrera F (2013) Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers. Pattern Recogn 46(12):3412–3424

    Article  Google Scholar 

  19. Galar M, Fernández A, Barrenechea E, Herrera F (2015) DRCW-OVO: distance-based relative competence weighting combination for one-vs-one strategy in multi-class problems. Pattern Recogn 48(1):28–42

    Article  Google Scholar 

  20. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064

    Article  Google Scholar 

  21. Garcia-Pedrajas N, Ortiz-Boyer D (2006) Improving multiclass pattern recognition by the combination of two strategies. IEEE Trans Pattern Anal Mach Intell 28(6):1001–1006

    Article  Google Scholar 

  22. García-Pedrajas N, Ortiz-Boyer D (2011) An empirical study of binary classifier fusion methods for multiclass classification. Inf Fusion 12(2):111–130

    Article  Google Scholar 

  23. Ghani R (2000) Using error-correcting codes for text classification. In: Proc. 17th international conf. on machine learning. Morgan Kaufmann, San Francisco, pp 303–310

  24. Giacinto G, Roli F (1999) Methods for dynamic classifier selection. In: Proceedings 10th international conference on image analysis and processing, pp 659–664

  25. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  26. Hastie T, Tibshirani R (1998) Classification by pairwise coupling. In: Proceedings of the 1997 conference on advances in neural information processing systems 10, NIPS ’97. MIT Press, Cambridge, MA, USA, pp 507–513

  27. Herremans D, Sörensen K, Martens D (2015) Classification and generation of composer-specific music using global feature models and variable neighborhood search. Comput Music J 39(3):71–91

    Article  Google Scholar 

  28. Hüllermeier E, Fürnkranz J, Cheng W, Brinker K (2008) Label ranking by learning pairwise preferences. Artif Intell 172(16):1897–1916

    Article  MathSciNet  MATH  Google Scholar 

  29. Hüllermeier E, Vanderlooy S (2010) Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting. Pattern Recogn 43(1):128–142

    Article  MATH  Google Scholar 

  30. Iman RL, Davenport JM (1980) Approximations of the critical region of the fbietkan statistic. Commun Stat Theory Methods 9(6):571–595

    Article  MATH  Google Scholar 

  31. Iwendi C, Khan S, Anajemba J, Mittal M, Alenezi M, Alazab M (2020) The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems. Sensors 20:2559

    Article  Google Scholar 

  32. John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence, UAI’95. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 338–345

  33. Kijsirikul B, Ussivakul N (2002) Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of the 2002 International Joint Conference on Neural Networks, vol 1. IEEE, pp 980–985

  34. Ko AH, Sabourin R, Britto AS Jr (2008) From dynamic classifier selection to dynamic ensemble selection. Pattern Recogn 41(5):1718–1731

    Article  MATH  Google Scholar 

  35. Ko J, Byun H (2003) Binary classifier fusion based on the basic decomposition methods. In: Proceedings of the 4th international conference on Multiple classifier systems. Springer, pp 146–155

  36. Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24(2):281–286

    Article  Google Scholar 

  37. Lebrun G, Lezoray O, Charrier C, Cardot H (2007) An ea multi-model selection for svm multiclass schemes. In: Proceedings of the 9th international work conference on artificial neural networks, IWANN’07. Springer, Berlin, pp 260–267

  38. Li Q, Song Y, Zhang J, Sheng VS (2020) Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering. Expert Syst Appl 147:113152

    Article  Google Scholar 

  39. Liepert M (2003) Topological fields chunking for german with SVM’s: Optimizing SVM-parameters with ga’s. In: Proceedings of the international conference on recent advances in natural language processing

  40. Liu H, Zheng W, Sun G, Shi Y, Leng Y, Lin P, Wang R, Yang Y, feng Gao J, Wang H, Iramina K, Ge S (2017) Action understanding based on a combination of one-versus-rest and one-versus-one multi-classification methods. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), pp 1–5

  41. Mckay C, Fujinaga I (2006) jsymbolic: a feature extractor for midi files. In: In proceedings of the international computer music conference, pp 302–305

  42. Mendialdua I, Echegaray G, Rodriguez I, Lazkano E, Sierra B (2016) Undirected cyclic graph based multiclass pair-wise classifier: classifier number reduction maintaining accuracy. Neurocomputing 171:1576–1590

    Article  Google Scholar 

  43. Mendialdua I, Martínez-Otzeta JM, Rodriguez-Rodriguez I, Ruiz-Vazquez T, Sierra B (2015) Dynamic selection of the best base classifier in one versus one. Knowl Based Syst 85:298–306

    Article  Google Scholar 

  44. Ng SSY, Tse PW, Tsui KL (2014) A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects. Sensors (Basel Switz) 14(1):1295–1321

    Article  Google Scholar 

  45. Platt JC (1999) Advances in kernel methods. chap. In: Fast training of support vector machines using sequential minimal optimization. MIT Press, Cambridge, pp 185–208

  46. Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin dags for multiclass classification. In: Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS'99). MIT Press, Cambridge, MA, USA, 547–553

  47. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco

    Google Scholar 

  48. Santos EMD, Sabourin R, Maupin P (2008) A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recogn 41(10):2993–3009

    Article  MATH  Google Scholar 

  49. Sapp CS (2005) Online database of scores in the humdrum file format. In: ISMIR 2005, 6th international conference on music information retrieval, London, UK, 11–15 September 2005, Proceedings, pp 664–665

  50. Sierra B, Lazkano E, Irigoien I, Jauregi E, Mendialdua I (2011) K nearest neighbor equality: giving equal chance to all existing classes. Inf Sci 181(23):5158–5168

    Article  Google Scholar 

  51. Souza BFD, De Carvalho AC, Calvo R, Ishii RP (2006) Multiclass SVM model selection using particle swarm optimization. In: 2006 sixth international conference on hybrid intelligent systems (HIS’06), pp 31–31

  52. Szepannek G, Bischl B, Weihs C (2009) On the combination of locally optimal pairwise classifiers. Eng Appl Artif Intell 22(1):79–85

    Article  Google Scholar 

  53. Tsymbal A, Pechenizkiy M, Cunningham P, Puuronen S (2008) Dynamic integration of classifiers for handling concept drift. Inf Fusion 9(1):56–68

    Article  Google Scholar 

  54. Uriz M, Paternain D, Jurio A, Bustince H, Galar M (2018) A study of different families of fusion functions for combining classifiers in the one-vs-one strategy. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations - 17th International Conference, IPMU 2018, Cádiz, Spain, Communications in Computer and Information Science, vol 854. Springer, pp 427–440

  55. Wang TY, Chiang HM (2009) One-against-one fuzzy support vector machine classifier: An approach to text categorization. Expert Syst Appl 36(6):10030–10034

    Article  Google Scholar 

  56. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Kotz S., Johnson N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics). Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4380-9_16

  57. Woods K, Kegelmeyer WP Jr, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19(4):405–410

    Article  Google Scholar 

  58. Xiao H, Xiao Z, Wang Y (2016) Ensemble classification based on supervised clustering for credit scoring. Appl Soft Comput 43(C):73–86

    Article  Google Scholar 

  59. Yan J, Zhang Z, Lin K, Yang F, Luo X (2020) A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks. Knowl Based Syst 198:105922

    Article  Google Scholar 

  60. Zhang C, Bi J, Xu S, Ramentol E, Fan G, Qiao B, Fujita H (2019) Multi-imbalance: an open-source software for multi-class imbalance learning. Knowl Based Syst 174:137–143

    Article  Google Scholar 

  61. Zhang ZL, Luo XG, García S, Tang JF, Herrera F (2017) Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme. Knowl Based Syst 125:53–63

    Article  Google Scholar 

  62. Zhou L, Wang Q, Fujita H (2017) One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies. Inf Fusion 36:80–89

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by the Basque Government Research Teams Grant (IT900-16) and the European Regional Development Fund (FEDER), Grant number RTI2018-093337-B-I00 (MCI/AEI/FEDER, UE).

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, San Sebastian, Spain

    Izaro Goienetxea, Igor Rodríguez & Basilio Sierra

  2. Department of Computer Languages and Systems, University of the Basque Country UPV/EHU, San Sebastian, Spain

    Iñigo Mendialdua

Authors
  1. Izaro Goienetxea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iñigo Mendialdua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Igor Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Basilio Sierra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Izaro Goienetxea.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goienetxea, I., Mendialdua, I., Rodríguez, I. et al. Problems selection under dynamic selection of the best base classifier in one versus one: PSEUDOVO. Int. J. Mach. Learn. & Cyber. 12, 1721–1735 (2021). https://doi.org/10.1007/s13042-020-01270-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01270-9

Keywords