Skip to main content

Concept Drift

  • Reference work entry
  • First Online:
Encyclopedia of Machine Learning and Data Mining
  • 319 Accesses

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 949.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  • Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66

    Google Scholar 

  • Chu F, Zaniolo C (2004) Fast and light boosting for adaptive mining of data streams. In: Advances in knowledge discovery and data mining. Lecture notes in computer science, vol 3056, pp 282–292. Springer, Berlin/New York

    Chapter  Google Scholar 

  • Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3:261–283

    Google Scholar 

  • Clearwater S, Cheng T-P, Hirsh H (1989) Incremental batch learning. In: Proceedings of the sixth international workshop on machine learning, Ithaca. Morgan Kaufmann, pp 366–370

    Chapter  Google Scholar 

  • Domingos P (1997) Context-sensitive feature selection for lazy learners. Artif Intell Rev 11:227–253. [Aha D (ed) Special issue on lazy learning.]

    Google Scholar 

  • Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. SIGMOD Rec 34(2):18–26

    Article  MATH  Google Scholar 

  • Harries M, Horn K (1996) Learning stable concepts in domains with hidden changes in context. In: Kubat M, Widmer G (eds) Learning in context-sensitive domains (workshop notes). 13th international conference on machine learning, Bari

    Google Scholar 

  • Harries MB, Sammut C, Horn K (1998) Extracting hidden context. Mach Learn 32(2):101–126

    Article  MATH  Google Scholar 

  • Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: KDD’01: proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 97–106

    Chapter  Google Scholar 

  • Kilander F, Jansson CG (1993) COBBIT – a control procedure for COBWEB in the presence of concept drift. In: Brazdil PB (ed) European conference on machine learning. Springer, Berlin, pp 244–261

    Google Scholar 

  • Kolter JZ, Maloof MA (2003) Dynamic weighted majority: a new ensemble method for tracking concept drift. In: Third IEEE international conference on data mining ICDM-2003, Melbourne. IEEE CS Press, pp 123–130

    Google Scholar 

  • Kubat M (1989) Floating approximation in time-varying knowledge bases. Pattern Recognit Lett 10:223–227

    Article  MATH  Google Scholar 

  • Kubat M (1992) A machine learning based approach to load balancing in computer networks. Cybern Syst J

    Google Scholar 

  • Kubat M (1996) Second tier for decision trees. In: Machine learning: proceedings of the 13th international conference. Morgan Kaufmann, San Francisco, pp 293–301

    Google Scholar 

  • Kubat M, Widmer G (1995) Adapting to drift in continuous domains. In: Proceedings of the eighth European conference on machine learning. Springer, Berlin, pp 307–310

    Google Scholar 

  • Mierswa I, Wurst M, Klinkenberg R, Scholz M, Euler T (2006) Yale: rapid prototyping for complex data mining tasks. In: KDD’06: proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 935–940

    Chapter  Google Scholar 

  • Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5:239–266

    Google Scholar 

  • Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo

    Google Scholar 

  • Salganicoff M (1993) Density adaptive learning and forgetting. In: Machine learning: proceedings of the tenth international conference. Morgan Kaufmann, San Mateo, pp 276–283

    Google Scholar 

  • Schlimmer JC, Granger RI Jr (1986a) Beyond incremental processing: tracking concept drift. In: Proceedings AAAI-86. Morgan Kaufmann, Los Altos, pp 502–507

    Google Scholar 

  • Schlimmer J, Granger R Jr (1986b) Incremental learning from noisy data. Mach Learn 1(3):317–354

    Google Scholar 

  • Turney PD (1993a) Exploiting context when learning to classify. In: Brazdil PB (ed) European conference on machine learning. Springer, Berlin, pp 402–407

    Google Scholar 

  • Turney PD (1993b) Robust classification with context sensitive features. In: Paper presented at the industrial and engineering applicatións of artificial intelligence and expert systems, Edinburgh

    Google Scholar 

  • Turney P, Halasz M (1993) Contextual normalization applied to aircraft gas turbine engine diagnosis. J Appl Intell 3:109–129

    Article  Google Scholar 

  • Wang H, Fan W, Yu PS, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: KDD’03: proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 226–235

    Chapter  Google Scholar 

  • Widmer G (1996) Recognition and exploitation of contextual clues via incremental meta-learning. In: Saitta L (ed) Machine learning: proceedings of the 13th international workshop. Morgan Kaufmann, San Francisco, pp 525–533

    Google Scholar 

  • Widmer G, Kubat M (1993) Effective learning in dynamic environments by explicit concept tracking. In: Brazdil PB (ed) European conference on machine learning. Springer, Berlin, pp 227–243

    Google Scholar 

  • Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23:69–101

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

Sammut, C., Harries, M. (2017). Concept Drift. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_153

Download citation

Publish with us

Policies and ethics