Lacking labels in the stream: Classifying evolving stream data with few labels

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5722 LNAI, Issue , 2009, Pages 552-562

  Woolam, Clay ^a   Masud, Mohammad M ^a   Khan, Latifur ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION MODELS; DATA STREAM; LABEL PROPAGATION; LABELED DATA; LABELED TRAINING DATA; SEMI-SUPERVISED CLUSTERING; STREAM CLASSIFICATION; STREAM DATA; TRAINING DOCUMENTS;

INTELLIGENT SYSTEMS; LABELS;

DATA COMMUNICATION SYSTEMS;

EID: 70349871421     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04125-9_58     Document Type: Conference Paper

References (13)

1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for on-demand classification of evolving data streams. IEEE Transactions on Knowledge and Data Engineering 18(5), 577-589 (2006)
2. Chen, S., Wang, H., Zhou, S., Yu, P.: Stop chasing trends: Discovering high order models in evolving data. In: Proc. ICDE, pp. 923-932 (2008)
3. Fan, W.: Systematic data selection to mine concept-drifting data streams. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, WA, USA, pp. 128-137 (2004)
4. Scholz, M., Klinkenberg., R.: An ensemble classifier for drifting concepts. In: Proc. Second International Workshop on Knowledge Discovery in Data Streams (IWKDDS), Porto, Portugal, October 2005, pp. 53-64 (2005)
5. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proc. ninth ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, DC, USA, pp. 226-235. ACM, New York (2003)
6. Yang, Y., Wu, X., Zhu, X.: Combining proactive and reactive predictions for data streams. In: Proc. KDD, pp. 710-715 (2005)
7. Masud, M., Gao, J., Khan, L., Han, J., Thuraisingham, B.: A practical approach to classify evolving data streams: Training with limited amount of labeled data. In: Proc. International Conference on Data Mining (ICDM), Pisa, Italy, December 15-19, pp. 929-934 (2008)
8. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Boston, MA, USA, pp. 71-80. ACM Press, New York (2000)
9. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proc. seventh ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), San Francisco, CA, USA, August 2001, pp. 97-106 (2001)
10. Gao, J., Fan, W., Han, J.: On appropriate assumptions to mine data streams. In: Proc. Seventh IEEE International Conference on Data Mining (ICDM), Omaha, NE, USA, October 2007, pp. 143-152 (2007)
11. Kolter, J., Maloof., M.: Using additive expert ensembles to cope with concept drift. In: Proc. International Conference on Machine Learning (ICML), Bonn, Germany, August 2005, pp. 449-456 (2005)
12. Bengio, Y., Delalleau, O., Le Roux, N.: Label propagation and quadratic criterion. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 193- 216. MIT Press, Cambridge (2006)
13. Woolam, C., Khan, L.: Multi-label large margin hierarchical perceptron. IJDMMM 1(1), 5-22 (2008)