Evolving Systems

, Volume 3, Issue 4, pp 251–271 | Cite as

Single-pass active learning with conflict and ignorance

Original Paper

Abstract

In this paper, we present a new methodology for conducting active learning in a single-pass on-line learning context. Single-pass active learning can be understood as an approach for reducing the annotation effort for users and operators in on-line classification problems, in which usually the true class labels of new incoming samples are usually unknown. This reduction in effort can be achieved by selecting the most informative samples, that is, those that contribute most to improving the predictive performance of incremental classifiers. Our approach builds upon certainty-based sample selection in connection with version-space reduction. Two new reliability concepts were investigated and developed in connection with evolving fuzzy classifiers: conflict and ignorance. Conflict models the extent to which a new query point lies in the conflict region between two or more classes and therefore reflects a level of certainty in the classifier’s prediction. Ignorance represents the distance of a new query point from the training samples seen so far. In extended form, it integrates the actual variability of the version space. The choice of the model architecture used for on-line classification scenarios (evolving fuzzy classifier) is clearly motivated in the paper. The results based on real-world binary and multi-class classification streaming data show that our single-pass active learning approach yields evolving classifiers whose performance is similar to that of classifiers using all samples for adaptation; however, the annotation effort in terms of the number of class label requests is reduced by up to 90 %.

Keywords

Active learning Incremental single-pass learning Conflict Ignorance Evolving fuzzy classifiers 

References

  1. Angelov P, Filev D, Kasabov N (2010a) Editorial to evolving systems. Evol Syst 1(1):1–2CrossRefGoogle Scholar
  2. Angelov P, Filev D, Kasabov N (2010b) Evolving intelligent systems—methodology and applications. Wiley, New YorkCrossRefGoogle Scholar
  3. Angelov P, Lughofer E, Zhou X (2008) Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst 159(23):3160–3182MATHCrossRefMathSciNetGoogle Scholar
  4. Angelov P, Zhou X (2008) Evolving fuzzy-rule-based classifiers from data streams. IEEE Trans Fuzzy Syst 16(6):1462–1475CrossRefGoogle Scholar
  5. Backer SD, Scheunders P (2001) Texture segmentation by frequency-sensitive elliptical competitive learning. Image Vis Comput 19(9–10):639–648CrossRefGoogle Scholar
  6. Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601–1604Google Scholar
  7. Bifet A, Kirkby R (2011) Data stream mining—a practical approach. Technical report, Department of Computer Sciences, University of Waikato, JapanGoogle Scholar
  8. Bordes A, Ertekin S, Weston J, Bottou L (2005) Fast kernel classifiers with online and active learning. J Mach Learn Res 6:1579–1619MATHMathSciNetGoogle Scholar
  9. Bouchachia A (2009) Incremental induction of classification fuzzy rules. In: IEEE workshop on evolving and self-developing intelligent systems (ESDIS) 2009. Nashville, USA, pp 32–39Google Scholar
  10. Bouchachia A (2010) An evolving classification cascade with self-learning. Evol Syst 1(3):143–160CrossRefGoogle Scholar
  11. Bouchachia A, Mittermeir R (2006) Towards incremental fuzzy classifiers. Soft Comput 11(2):193–207CrossRefGoogle Scholar
  12. Chu W, Zinkevich M, Li L, Thomas A, Zheng B (2011) Unbiased online active learning in data streams. In: Proceedings of the KDD 2011. San Diego, CaliforniaGoogle Scholar
  13. Cohn D, Atlas L, Ladner R (1994) Improving generalization with active learning. Mach Learn 15(2):201–221Google Scholar
  14. Cohn D, Ghahramani Z, Jordan M (1996) Active learning with statistical models. J Artif Intell Res 4(1):129–145MATHGoogle Scholar
  15. Condurache A (2002) A two-stage-classifier for defect classification in optical media inspection. In: Proceedings of the 16th international conference on pattern recognition (ICPR’02), vol 4. Quebec City, Canada, pp 373–376Google Scholar
  16. Dagan I, Engelson S (1995) Committee-based sampling for training probabilistic classifier. In: Proceedings of 12th international conference on machine learning, pp 150–157Google Scholar
  17. Diehl C, Cauwenberghs G (2003) SVM incremental learning, adaptation and optimization. In: Proceedings of the international joint conference on neural networks, vol 4. Boston, pp 2685–2690Google Scholar
  18. Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining. Boston, MA, pp 71–80Google Scholar
  19. Domingos P, Hulten G (2001) Catching up with the data: research issues in mining data streams. In: Proceedings of the workshop on research issues in data mining and knowledge discovery. Santa Barbara, CAGoogle Scholar
  20. Donmez P, Carbonell J (2008) Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In: Proceedings of the CIKM 2008 conference. Napa Valley, CaliforniaGoogle Scholar
  21. Eitzinger C, Heidl W, Lughofer E, Raiser S, Smith J, Tahir M, Sannen D, van Brussel H (2010) Assessment of the influence of adaptive components in trainable surface inspection systems. Mach Vis Appl 21(5):613–626CrossRefGoogle Scholar
  22. Fukumizu K (2000) Statistical active learning in multilayer perceptrons. IEEE Trans Neural Netw 11(1):17–26CrossRefGoogle Scholar
  23. Fürnkranz J (2002) Round robin classification. J Mach Learn Res 2:721–747MATHMathSciNetGoogle Scholar
  24. Gacto M, Alcala R, Herrera F (2011) Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf Sci 181(20):4340–4360CrossRefGoogle Scholar
  25. Gama J (2010) Knowledge discovery from data streams. Chapman and Hall/CRC, Boca RatonMATHCrossRefGoogle Scholar
  26. Hartert L, Sayed-Mouchaweh M, Billaudel P (2010) A semi-supervised dynamic version of fuzzy k-nearest neighbors to monitor evolving systems. Evol Syst 1(1):3–15CrossRefGoogle Scholar
  27. Hisada M, Ozawa S, Zhang K, Kasabov N (2010) Incremental linear discriminant analysis for evolving feature spaces in multitask pattern recognition problems. Evol Syst 1(1):17–27CrossRefGoogle Scholar
  28. Hu W, Hu W, Xi N, Maybank S (2009) Unsupervised active learning based on hierarchical graph-theoretic clustering. IEEE Trans Syst Man Cybern Part B Cybern 39(5):1147–1161CrossRefGoogle Scholar
  29. Hühn J, Hüllermeier E (2009) FR3: A fuzzy rule learner for inducing reliable classifiers. IEEE Trans Fuzzy Syst 17(1):138–149CrossRefGoogle Scholar
  30. Hüllermeier E, Brinker K (2008) Learning valued preference structures for solving classification problems. Fuzzy Sets Syst 159(18):2337–2352MATHCrossRefGoogle Scholar
  31. Ishibuchi H, Nakashima T (2001) Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 9(4):506–515CrossRefGoogle Scholar
  32. Jackson P (1999) Introduction to expert systems. Addison Wesley Pub Co Inc., Edinburgh GateGoogle Scholar
  33. Kruse R, Gebhardt J, Palm R (1994) Fuzzy systems in computer science. Verlag Vieweg, WiesbadenCrossRefGoogle Scholar
  34. Kuncheva L (2000) Fuzzy classifier design. Physica-Verlag, HeidelbergMATHCrossRefGoogle Scholar
  35. Lemos A, Caminhas W, Gomide F (2012) Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Inf Sci. (in press). doi:10.1016/j.ins.2011.08.030
  36. Leng G, McGinnity T, Prasad G (2005) An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst 150(2):211–243MATHCrossRefMathSciNetGoogle Scholar
  37. Lewis D, Catlett J (1994) Heterogeneous uncertainty sampling for supervised learning. In: Proceedings of the 11th international conference on machine learning. New Brunswick, New Jersey, pp 148–156Google Scholar
  38. Lughofer E (2011) All-pairs evolving fuzzy classifiers for on-line multi-class classification problems. In: Proceedings of the EUSFLAT 2011 conference. Elsevier, Aix-Les-Bains, France, pp 372–379Google Scholar
  39. Lughofer E (2011) Evolving fuzzy systems—methodologies, advanced concepts and applications. Springer, BerlinMATHCrossRefGoogle Scholar
  40. Lughofer E (2011) On-line incremental feature weighting in evolving fuzzy classifiers. Fuzzy Sets Syst 163(1):1–23MATHCrossRefMathSciNetGoogle Scholar
  41. Lughofer E, Angelov P, Zhou X (2007) Evolving single- and multi-model fuzzy classifiers with FLEXFIS-Class. In: Proceedings of FUZZ-IEEE 2007. London, UK, pp 363–368Google Scholar
  42. Lughofer E, Bouchot JL, Shaker A (2011) On-line elimination of local redundancies in evolving fuzzy systems. Evol Syst 2(3):165–187CrossRefGoogle Scholar
  43. Lughofer E, Smith JE, Caleb-Solly P, Tahir M, Eitzinger C, Sannen D, Nuttin M (2009) Human-machine interaction issues in quality control based on on-line image classification. IEEE Trans Syst Man Cybern Part A Syst Hum 39(5):960–971CrossRefGoogle Scholar
  44. Muslea I (2000) Active learning with multiple views. PhD thesis, University of Southern CaliforniaGoogle Scholar
  45. Nauck D, Kruse R (1998) NEFCLASS-X–a soft computing tool to build readable fuzzy classifiers. BT Technol J 16(3):180–190CrossRefGoogle Scholar
  46. Oza NC, Russell S (2001) Online bagging and boosting. In: Proceedings of the 8th international workshop on artificial intelligence and statistics 2001 (AI and STATISTICS 2001). Morgan Kaufmann, Key West, Florida, pp 105–112Google Scholar
  47. Pang S, Ozawa S, Kasabov N (2005) Incremental linear discriminant analysis for classification of data streams. IEEE Trans Syst Men Cybern Part B Cybern 35(5):905–914CrossRefGoogle Scholar
  48. Roy N, Mccallum A (2001) Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of the 18th international conference on machine learning. Morgan Kaufmann, pp 441–448Google Scholar
  49. Schölkopf B, Smola A (2002) Learning with kernels: support vector machines, regularization, optimization and beyond. MIT Press, LondonGoogle Scholar
  50. Sculley D (2007) Online active learning methods for fast label efficient spam filtering. In: Proceedings of the fourth conference on email and AntiSpam. Mountain View, CaliforniaGoogle Scholar
  51. Settles B (2010) Active learning literature survey. Technical report, Computer Sciences Technical Report 1648, University of Wisconsin MadisonGoogle Scholar
  52. Shilton A, Palaniswami M, Ralph-D D, Tsoi A-C (2005) Incremental training of support vector machines. IEEE Trans Neural Netw 16(1):114–131CrossRefGoogle Scholar
  53. Thompson C, Califf M, Mooney R (1999) Active learning for natural language parsing and information extraction. In: Proceedings of 16th international conference on machine learning. Bled, Slovenia, pp 406–414Google Scholar
  54. Tong S, Koller D (2001) Support vector machine active learning with application to text classification. J Mach Learn Res 2:45–66Google Scholar
  55. Tuia D, Volpi M, Copa L, Kanevski M, Muñoz-Marí J (2011) A survey of active learning algorithms for supervised remote sensing image classification. IEEE J Sel Topics Signal Process 5(3):606–617CrossRefGoogle Scholar
  56. Utgoff P (1989) Incremental induction of decision trees. Mach Learn 4(2):161–186CrossRefGoogle Scholar
  57. Vapnik V (1998) Statistical learning theory. Wiley, New YorkMATHGoogle Scholar
  58. Varma M, Zisserman A (2004) Unifying statistical texture classification frameworks. Image Vis Comput 22:1175–1183CrossRefGoogle Scholar
  59. Zvarova J (2006) Decision support systems in medicine. In: Zielinski K, Duplaga M, Ingram D (eds) Information technology solutions for healthcare. Springer, London, pp 182–204Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Knowledge-based Mathematical SystemsJohannes Kepler University of LinzLinzAustria

Personalised recommendations