Adaptive preprocessing for streaming data

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 2, 2014, Pages 309-321

  Žliobaite, Indre ^a   Gabrys, Bogdan ^a  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

adaptive preprocessing; Concept drift; streaming data

Indexed keywords

ADAPTIVE PREDICTORS; ADAPTIVE PREPROCESSING; CONCEPT DRIFTS; PREDICTION ACCURACY; REFERENCE FRAMEWORKS; STREAMING DATA; SUPERVISED LEARNING APPROACHES; SYSTEMATIC RESEARCH;

COMPUTATIONAL METHODS; INFORMATION SYSTEMS;

RESEARCH;

EID: 84891754192     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2012.147     Document Type: Article

References (21)

1. A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavalda, New Ensemble Methods for Evolving Data Streams, Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '09), pp. 139-148, 2009.
2. E. Ikonomovska, J. Gama, and S. Dzeroski, Learning Model Trees from Evolving Data Streams, Data Mining Knowledge Discovery, vol. 23, no. 1, pp. 128-168, 2011.
3. P. Kadlec and B. Gabrys, Architecture for Development of Adaptive on-Line Prediction Models, Memetic Computing, vol. 1, no. 4, pp. 241-269, 2009.
4. M. Masud, J. Gao, L. Khan, J. Han, and B. Thuraisingham, Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints, IEEE Trans. Knowledge and Data Eng., vol. 23, no. 6, pp. 859-874, June 2011.
5. J. Kolter and M. Maloof, Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts, J. Machine Learning Research, vol. 8, pp. 2755-2790, Dec. 2007.
6. L. Minku, A. White, and X. Yao, The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift, IEEE Trans. Knowledge and Data Eng., vol. 22, no. 5, pp. 730-742, May 2010.
7. T. Breur Toms Ten Data Tips, http://www.xlntconsulting.com/newsletter- archive/data-preparation-december-2007.html, 2007.
8. I. Witten, E. Frank, and M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, third ed. Morgan Kaufmann, 2011.
9. E. Bingham and H. Mannila, random Projection in Dimensionality Reduction: Applications to Image and Text Data, Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 245-250, 2001.
10. G. Widmer and M. Kubat, Effective Learning in Dynamic Environments by Explicit Context Tracking, Proc. European Conf. Machine Learning (ECML '93), pp. 227-243, 1993.
11. M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han, and B. Thuraisingham, Addressing Concept-Evolution in Concept-Drifting Data Streams, Proc. IEEE 10th Int'l Conf. Data Mining (ICDM '10), 2010.
12. G. Widmer and M. Kubat, Learning in the Presence of Concept Drift and Hidden Contexts, Machine Learning, vol. 23, pp. 69-101, 1996. (Pubitemid 126737384)
13. I. -Zliobait- e, Adaptive Training Set Formation, PhD dissertation, Vilnius Univ., 2010.
14. R. Klinkenberg, Learning Drifting Concepts: Example Selection versus Example Weighting, Intelligent Data Analysis, vol. 8, pp. 281-300, 2004.
15. A. Tsymbal, M. Pechenizkiy, P. Cunningham, and S. Puuronen, Dynamic Integration of Classifiers for Handling Concept Drift, Information Fusion, vol. 9, pp. 56-68, 2008. (Pubitemid 47589061)
16. M.M.Q. Chen, J. Gao, L. Khan, J. Han, and B. Thuraisingham, Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space, Proc. European Conf. Machine Learning and Knowledge Discovery Databases: Part II (ECML PKDD '10), pp. 337-352, 2010.
17. I. Katakis, G. Tsoumakas, and I. Vlahavas, Dynamic Feature Space and Incremental Feature Selection for the Classification of Textual Data Streams, Proc. ECML/PKDD '06 Int'l Workshop Knowledge Discovery from Data Streams, pp. 107-116, 2006.
18. B. Wenerstrom and C. Giraud-Carrier, Temporal Data Mining in Dynamic Feature Spaces, Proc. Sixth Int'l Conf. Data Mining (ICDM '06), pp. 1141-1145, 2006.
19. C. Anagnostopoulos, D. Tasoulis, D. Hand, and N. Adams, Online Optimization for Variable Selection in Data Streams, Proc. 18th European Conf. Artificial Intelligence (ECAI '08), pp. 132-136, 2008.
20. C. Anagnostopoulos, N. Adams, and D. Hand, Deciding what to Observe Next: Adaptive Variable Selection for Regression in Multivariate Data Streams, Proc. ACM Symp. Applied Computing (SAC '08), pp. 961-965, 2008.
21. H. Ruda, Adaptive Preprocessing for on-Line Learning with Adaptive Resonance Theory (Art) Networks, Proc. IEEE Workshop Neural Networks for Signal Processing (NNSP), 1995.