Distributed pasting of small votes

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

Volumn 2364, Issue , 2002, Pages 52-61

  Chawla, N V ^a   Hall, L O ^a   Bowyer, K W ^b   Moore, T E ^a   Kegelmeyer, W P ^c  

^a University of South Florida   (United States)

^b University of Notre Dame   (United States)

^c SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

DISTRIBUTED ENVIRONMENTS; ENSEMBLE METHODS; MASSIVE DATA SETS; SCALE-UP;

CLASSIFICATION (OF INFORMATION);

EID: 84947592660     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45428-4_5     Document Type: Conference Paper

References (20)

1. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, Vol 36, 105-139. Kluwer (1999).
2. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences (1998).
3. Breiman, L. Bagging predictors. Machine Learning, Vol 24. Kluwer (1996) 123–140.
4. Breiman, L.: Pasting small votes for classification in large databases and on-line. Machine Learning, Vol 36. Kluwer (1999) 85–103.
5. Chan, P., Stolfo, S.: Towards parallel and distributed learning by meta-learning. AAAI Workshop on Knowledge Discovery and Databases. (1993) 227-240.
6. Chawla, N., Eschrich, S., Hall, L.O.; Creating ensembles of classifiers. First IEEE International Conference on Data Mining. (2000).
7. Chawla, N.V., Moore, T.E., Bowyer, K.W., Hall, L.O., Springer, C., Kegelmeyer, W.P.: Bagging is a small dataset phenomenon. International Conference of Computer Vision and Pattern Recognition (CVPR). (2000) 684–689.
8. Dietterich, T.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, Vol 40. Kluwer (2000) 139–158.
9. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. Thirteenth International Conference on Machine Learning. (1996).
10. Hall, L.O., Chawla, N.V., Bowyer, K.W., Kegelmeyer, W.P.: Learning rules from distributed data. Workshop of Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (1999).
11. Jones, D.: Protein secondary structure prediction based on decision-specific scoring matrices. Journal of Molecular Biology, Vol 292. (1999) 195–202.
12. Latinne, P., Debeir, O., Decaestecker, C.: Different ways of weakening decision trees and their impact on classification accuracy of DT combination. First International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science, Vol 1857. Springer-Verlag, (2000) 200–210.
13. Lazarevic, A., Obradovic, Z.: The distributed boosting algorithm. Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2000).
14. Musick, R., Catlett, J., Russell, S.. Decision theoretic subsampling for induction on large databases. Tenth International Conference on Machine Learning, A mherst, MA. (1993) 212-219.
15. Provost, F.J., Hennessy D.N.: Scaling up: Distributed machine learning with cooperation. Thirteenth National Conference on Artificial Intelligence. (1996) 74-79.
16. Protein Data Bank. http://www.rcsb.org/pdb/
17. Provost, F., Jensen D., Oates, T.: Efficient progressive sampling. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (1999) 23-32.
18. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufman San Mateo, CA(1992).
19. Sandia National Labs.: ASCI RED, the world’s first TeraFLOPS supercomputer. http://www.sandia.gov/ASCI/Red.
20. Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://www.acm.org/sigkdd/kdd2001/ (2001).