Concept drifting detection on noisy streaming data in random ensemble decision trees

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

Volumn 5632 LNAI, Issue , 2009, Pages 236-250

  Li, Peipei ^a,b   Hu, Xuegang ^a   Liang, Qianhui ^b   Gao, Yunjun ^b,c  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Concept Drift; Data Streams; Ensemble Decision Trees; Noise

Indexed keywords

CLASSIFICATION ALGORITHM; CONCEPT DRIFT; CONCEPT DRIFTING; CONCEPT DRIFTS; DATA STREAM; DATA STREAMS; ENSEMBLE CLASSIFICATION; ENSEMBLE DECISION TREES; INDUCTIVE LEARNING; LIGHT WEIGHT; NOISE; NOISY DATA STREAMS; PREDICTIVE ACCURACY; RUNTIMES; STREAMING DATA; TIME AND SPACE;

DATA COMMUNICATION SYSTEMS; DATA MINING; DECISION TREES; PATTERN RECOGNITION; ROBOT LEARNING;

LEARNING ALGORITHMS;

EID: 70350214945     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03070-3_18     Document Type: Conference Paper

References (25)

1. Street, W., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: 7th ACM SIGKDD international conference on Knowledge Discovery and Data mining, KDD 2001, pp. 377-382. ACM Press, New York (2001)
2. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining Concept-Drifting Data Streams Using Ensemble Classifiers. In: 9th ACM SIGKDD international conference on Knowledge Discovery and Data mining, KDD 2003, pp. 226-235. ACM Press, New York (2003)
3. Fan, W.: Streamminer: a classifier ensemble-based engine to mine concept-drifting data streams. In: 30th international conference on Very Large Data Bases, VLDB 2004, pp. 1257-1260. VLDB Endowment (2004)
4. Chu, F., Wang, Y., Zaniolo, C.: An adaptive learning approach for noisy data streams. In: 4th IEEE International Conference on Data Mining, pp. 351-354. IEEE Computer Science, Los Alamitos (2004)
5. Gama, J., Fernandes, R., Rocha, R.: Decision trees for mining data streams. Intelligent Data Analysis 10, 23-45 (2006)
6. Scholz, M., Klinkenberg, R.: Boosting Classifiers for Drifting Concepts. Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams 11(1), 3-28 (2007)
7. Hoeffding, W.: Probability inequalities for sums of bounded random variabless. Journal of the American Statistical Association 58(301), 13-30 (1963)
8. Castillo, G., Gama, J., Medas, P.: Adaptation to Drifting Concepts. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 279-293. Springer, Heidelberg (2003)
9. Gehrke, J., Ganti, V., Ramakrishnan, R., Loh, W.Y.: Boat-optimistic decision tree construction. In: 1999 ACM SIGMOD International Conference on Management of Data, pp. 169-180. ACM Press, New York (1999)
10. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: 7th ACM SIGKDD international conference on Knowledge Discovery and Data mining, KDD 2001, pp. 97-106 (2001)
11. Li, P., Hu, X., Wu, X.: Mining concept-drifting data streams with multiple semirandom decision trees. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS, vol. 5139, pp. 733-740. Springer, Heidelberg (2008)
12. Ho, T.K.: Random decision forests. In: 3rd International Conference on Document Analysis and Recognition, pp. 278-282. IEEE Computer Society, Los Alamitos (1995)
13. Abdulsalam, H., Skillicorn, D.B., Martin, P.: Classifying Evolving Data Streams Using Dynamic Streaming Random Forests. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 643-651. Springer, Heidelberg (2008)
14. 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(5), 711-724 (2007)
15. Yang, Y., Wu, X., Zhu, X.: Combining Proactive and Reactive Predictions for Data Streams. In: 11th ACM SIGKDD international conference on Knowledge Discovery in Data mining, KDD 2005, pp. 710-715. ACM Press, New York (2005)
16. Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
17. Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)
18. Quinlan, R.J.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
19. Shafer, J., Agrawal, R., Mehta, M.: Sprint: A scalable parallel classifier for data mining. In: 22th International Conference on Very Large Data Bases, VLDB 1996, pp. 544-555. Morgan Kaufmann, San Francisco (1996)
20. KDDCUP 1999 DataSet, http://kdd.ics.uci.edu//databases/kddcup99/kddcup99. html
21. Yahoo! Shopping Web Services, http://developer.yahoo.com/everything.html
22. Li, P., Liang, Q., Wu, X., Hu, X.: Parameter Estimation in Semi-Random Decision Tree Ensembling on Streaming Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 376-388. Springer, Heidelberg (2009)
23. Wikipedia, http://en.wikipedia.org/wiki/Data-stream
24. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9, 1545-1588 (1997)
25. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832-844 (1998)