Fast perceptron decision tree learning from evolving data streams

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

Volumn 6119 LNAI, Issue PART 2, 2010, Pages 299-310

  Bifet, Albert ^a   Holmes, Geoff ^a   Pfahringer, Bernhard ^a   Frank, Eibe ^a  

^a UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION FUNCTIONS; DATA STREAM; DECISION TREE LEARNING; EVALUATION STUDY; LEARNING STRATEGY; MAJORITY VOTE; NAIVE BAYES; NAIVE BAYES MODELS; PERCEPTRON; REAL-WORLD DATASETS; RUNTIMES; SIGMOID ACTIVATION FUNCTION; SQUARED ERRORS;

CYBERNETICS; DATA COMMUNICATION SYSTEMS; DATA MINING; DECISION TREES; FUNCTION EVALUATION; PATTERN RECOGNITION SYSTEMS; PLANT EXTRACTS;

TREES (MATHEMATICS);

EID: 79956323714     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13672-6_30     Document Type: Conference Paper

References (26)

1. Asuncion, A., Newman, D.: UCI machine learning repository (2007)
2. Bennett, K., Cristianini, N., Shawe-Taylor, J., Wu, D.: Enlarging the margins in perceptron decision trees. Machine Learning 41(3), 295-313 (2000)
3. Bifet, A., Gavaldà, R.: Learning from time-changing data with adaptive windowing. In: SDM (2007)
4. Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R.: New ensemble methods for evolving data streams. In: KDD, pp. 139-148 (2009)
5. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)
6. Domingos, P., Hulten, G.: Mining high-speed data streams. In: KDD, pp. 71-80 (2000)
7. Frank, E., Wang, Y., Inglis, S., Holmes, G., Witten, I.H.: Using model trees for classification. Machine Learning 32(1), 63-76 (1998)
8. Gama, J.: On Combining Classification Algorithms. VDM Verlag (2009)
9. Gama, J., Medas, P., Castillo, G., Rodrigues, P.P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286-295. Springer, Heidelberg (2004) (Pubitemid 39751638)
10. Gama, J., Rocha, R., Medas, P.: Accurate decision trees for mining high-speed data streams. In: KDD, pp. 523-528 (2003)
11. Harries, M.: Splice-2 comparative evaluation: Electricity pricing. Technical report, The University of South Wales (1999)
12. Holmes, G., Kirkby, R., Pfahringer, B.: Stress-testing Hoeffding trees. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 495-502. Springer, Heidelberg (2005)
13. Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive Online Analysis (2007), http://sourceforge.net/projects/moa-datastream
14. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: KDD, pp. 97-106 (2001)
15. Ikonomovska, E., Gama, J.: Learning model trees from data streams. Discovery Science, 52-63 (2008)
16. Ikonomovska, E., Gama, J., Sebastião, R., Gjorgjevik, D.: Regression trees from data streams with drift detection. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 121-135. Springer, Heidelberg (2009)
17. Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Machine Learning 59(1-2), 161-205 (2005) (Pubitemid 40890416)
18. Murthy, S.K.: Automatic construction of decision trees from data: A multidisciplinary survey. Data Min. Knowl. Discov. 2(4), 345-389 (1998) (Pubitemid 128695483)
19. Oza, N., Russell, S.: Online bagging and boosting. In: Artificial Intelligence and Statistics 2001, pp. 105-112. Morgan Kaufmann, San Francisco (2001)
20. Oza, N.C., Russell, S.J.: Experimental comparisons of online and batch versions of bagging and boosting. In: KDD, pp. 359-364 (2001)
21. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics 21(3), 660-674 (1991) (Pubitemid 21703512)
22. Schlimmer, J.C., Fisher, D.H.: A case study of incremental concept induction. In: AAAI, pp. 496-501 (1986)
23. Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: KDD, pp. 377-382 (2001)
24. Utgoff, P.E.: Perceptron trees: A case study in hybrid concept representations. In: AAAI, pp. 601-606 (1988)
25. Velte, T., Velte, A., Elsenpeter, R.: Cloud Computing, A Practical Approach. McGraw-Hill, Inc., New York (2010)
26. Zhou, Z., Chen, Z.: Hybrid decision tree. Knowledge-based systems 15(8), 515-528 (2002) (Pubitemid 34732325)