Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several methods have been proposed that are able to learn in the presence of concept drift, few consider concept recurrence and integration of context. In this work, we extend existing drift detection methods to deal with this problem by exploiting context information associated with learned decision models in situations where concepts reappear. The preliminary experimental results demonstrate the effectiveness of the proposed approach for data stream classification problems with recurring concepts.

[1]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[2]  Geoff Hulten,et al.  Catching up with the Data: Research Issues in Mining Data Streams , 2001, DMKD.

[3]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[4]  João Gama,et al.  Learning with Drift Detection , 2004, SBIA.

[5]  Arkady B. Zaslavsky,et al.  Towards a theory of context spaces , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[6]  Claude Sammut,et al.  Extracting Hidden Context , 1998, Machine Learning.

[7]  Ralf Klinkenberg,et al.  Boosting classifiers for drifting concepts , 2007, Intell. Data Anal..

[8]  Xindong Wu,et al.  Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams , 2006, Data Mining and Knowledge Discovery.

[9]  William Nick Street,et al.  A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.

[10]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[11]  Vipin Kumar,et al.  Chapman & Hall/CRC Data Mining and Knowledge Discovery Series , 2008 .

[12]  Marcus A. Maloof,et al.  Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..

[13]  Gregory D. Abowd,et al.  A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications , 2001, Hum. Comput. Interact..

[14]  Geoff Holmes,et al.  MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..

[15]  A. Bifet,et al.  Early Drift Detection Method , 2005 .

[16]  KlinkenbergRalf.  Learning drifting concepts: Example selection vs. example weighting , 2004 .

[17]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[18]  Mohammed J. Zaki.  Editorial: Online, Interactive, and Anytime Data Mining , 2002, SIGKDD Explor..

[19]  JefI’rty C. Schlirrlrrer.  Beyond incremental processing : Tracking concept drift , 1999 .

[20]  P. Brézillon,et al.  Contextual knowledge sharing and cooperation in intelligent assistant systems , 1999 .

[21]  Mohamed Medhat Gaber,et al.  Learning from Data Streams: Processing Techniques in Sensor Networks , 2007 .

[22]  Ralf Klinkenberg,et al.  Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..

[23]  Raj Bhatnagar,et al.  Tracking recurrent concept drift in streaming data using ensemble classifiers , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

[24]  Gerhard Widmer,et al.  Tracking Context Changes through Meta-Learning , 1997, Machine Learning.

[25]  Xindong Wu,et al.  Combining proactive and reactive predictions for data streams , 2005, KDD '05.

[26]  João Gama,et al.  Tracking Recurring Concepts with Meta-learners , 2009, EPIA.

[27]  Thorsten Joachims,et al.  Detecting Concept Drift with Support Vector Machines , 2000, ICML.

[28]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[29]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[30]  Peter D. Turney.  Exploiting Context When Learning to Classify , 1993, ECML.

[31]  M. Harries.  SPLICE-2 Comparative Evaluation: Electricity Pricing , 1999 .

[32]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[33]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

[34]  Grigorios Tsoumakas,et al.  Tracking recurring contexts using ensemble classifiers: an application to email filtering , 2009, Knowledge and Information Systems.