Streaming Random Forests

Proceedings of the International Database Engineering and Applications Symposium, IDEAS

Volumn , Issue , 2007, Pages 225-232

  Abdulsalam, Hanady ^a   Skillicorn, David B ^a   Martin, Patrick ^a  

^a QUEEN S UNIVERSITY   (Canada)

Author keywords

Classification; Data mining; Data stream classification; Decision trees; Random forests

Indexed keywords

APPLICATIONS; DATA MINING; DATA STRUCTURES; DATABASE SYSTEMS; DECISION THEORY; DECISION TREES; INFORMATION MANAGEMENT; MATHEMATICAL MODELS; RANDOM PROCESSES;

CLASSIFICATION; CLASSIFICATION ACCURACIES; DATA RECORDS; DATA SETS; DATA STREAMS; DATA-STREAM CLASSIFICATION; HIGH FLOWS; RANDOM FORESTS; STREAMING DATUMS;

TREES (MATHEMATICS);

EID: 47949101478     PISSN: 10988068     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IDEAS.2007.4318108     Document Type: Conference Paper

References (20)

1. Random Forest FORTRAN Code. Available from http://www.stat.berkeley.edu/ breiman/RandomForests/cc_software.htm/.
2. Forest CoverType data set. Available from http://kdd.ics.uci.edu/.
3. C. Aggarwal, J. Han, J. Wang, and P. Yu. Sa framework for clustering evolving data streams. In Proceedings of 29th International Conference on Very Large Data Bases(VLDB), pages 81-92. Berlin, Germany, 2003.
4. J. Blackard. Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types. PhD thesis, Department of Forest Sciences. Colorado State University, Fort Collins, Colorado, 1998.
5. L. Breiman. Random forests. Technical Report, 1999. Available at www.stat.berkeley.edu.
6. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth International, Belmont, Ca., 1984.
7. A. Bulut and A. Singh. A unified framework for monitoring data streams in real time. In Proceedings of the 21st International Conference on Data Engineering (ICDE), pages 44-55. Tokyo, Japan, 2005.
8. F. Chu, Y. Wang, and C. Zaniolo. An adaptive learning approach for noisy data streams. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pages 351-354. Brighton, UK, November 2004.
9. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 71-80. Boston, MA, August 2000.
10. W. Fan. A systematic data selection to mine concept-drifting data streams. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 128-137. Seattle, Washington, August 2004.
11. M. Gaber, S. Krishnaswamy, and A. Zaslavsky. Costefficient mining techniques for data streams. In Proceedings of the 1st Australasian Workshop on Data Mining and Web Intelligence (DMWI), pages 81-92. Dunedin, New Zealand, 2003.
12. J. Gama, P. Medas, and R. Rocha. Forest trees for on-line data. In Proceedings of the 2004 ACM Symposium on Applied Computing (SAC), pages 632-636. Nicosia, Cyprus, March 2004.
13. G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD), pages 97-106. San Francisco, CA, August 2001.
14. R. Jin and G. Agrawal. Efficient decision tree construction on streaming data. In Proceedings of 9th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 571-576. Washington, DC, August 2003.
15. D. Kifer, S. Ben-David, and J. Gehrke. Detecting change in data streams. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB), pages 180-191. Toronto, Canada, 2004.
16. B. Krishnamurthy, S. Sen, Y. Zhang, and Y. Chen. Sketch-based change detection: methods, evaluation, and applications. In Proceedings of the 3rd ACM SIGCOMM Internet Measurement Conference (IMC), pages 234-247. Miami Beach, FL, 2003.
17. G. Melli. Scds-a synthetic classification data set generator. Simon Fraser University, School of Computer Science, 1997.
18. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 226-235. Washington, DC, August 2003.
19. X. Zhu, X. Wu, and Y. Yang. Dynamic classifier selection for effective mining from noisy data streams. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pages 305-312. Brighton, UK, November 2004.
20. Y. Zhu and D. Shasha. Statistical monitoring of thousands of data streams in real time. In Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), pages 358-369. Hong Kong, China, 2002.