Mining concept-drifting data streams with multiple semi-random decision trees

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

Volumn 5139 LNAI, Issue , 2008, Pages 733-740

  Li, Peipei ^a   Hu, Xuegang ^a   Wu, Xindong ^a,b  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b University of Vermont   (United States)

Author keywords

Classification; Concept Drift; Data Streams; Random Decision Trees

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STREAMS; DECISION TREES; NOISE POLLUTION; TREES (MATHEMATICS);

CLASSIFICATION ALGORITHM; CLASSIFICATION FUNCTIONS; CONCEPT DRIFTS; DECISION-TREE ALGORITHM; EMPIRICAL STUDIES; INCREMENTAL ALGORITHM; RANDOM DECISION TREES; TRAINING AND TESTING;

DATA MINING;

EID: 68749105782     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88192-6_78     Document Type: Conference Paper

References (12)

1. Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101 (1996)
2. Hulten, G., Spencer, L., Domingos, P.: Mining Time-Changing Data Streams. In: ACM KDD Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp. 97-106 (2001)
3. Fan, W.: StreamMiner: A Classifier Ensemble-based Engine to Mine Concept-drifting Data Streams. In: 30th VLDB Conference, Toronto, Canada (2004)
4. Zhang, Y., Jin, X.: An automatic construction and organization strategy for ensemble learning on data streams. In: ACM SIGMOD Record, pp. 28-33 (2006)
5. Hu, X., Li, P., Wu, X., Wu, G.: A Semi-Random Multiple Decision-Tree Algorithm for Mining Data Streams. Journal of Computer Science and Technology 22, 711-724 (2007)
6. Hoeffding, W.: Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58, 13-30 (1963)
7. The UCI KDD Archive, http://kdd.ics.uci.edu//databases/kddcup99/kddcup99. html
8. Breiman, L.: Random forests. Machine Learning 45, 5-32 (2001)
9. Yang, Y., Zhu, X., Wu, X.: Combining Proactive and Reactive Predictions for Data Streams. In: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, pp. 710-715 (2005)
10. Stanley, K.O.: Learning concept drift with a committee of decision trees. Technical Report AI-03-302, Department of Computer Sciences, University of Texas at Austin (2003)
11. Salganicoff, M.: Tolerating concept and sampling shift in lazy learning using prediction error context switching. Artificial Intelligence 11, 133-155 (1997)
12. Kolrer, J.Z., Marcus, A.: Dynamic Weighted majority: A new Ensemble Method for Tracking Concept Drift. In: 3rd International IEEE Conference on Data Mining, Melbourne, Florida, USA, pp. 123-130 (2003)