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Cited by 1,668

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

SMOTEBoost: Improving Prediction of the Minority Class in Boosting

Chawla, Nitesh V., Lazarevic, Aleksandar, Hall, Lawrence O., Bowyer, Kevin W.·2003·Lecture Notes in Computer Science
DOI 10.1007/978-3-540-39804-2_12
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Research works printed but not linked: 20

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  2. Joshi, M., Kumar, V., Agarwal, R.: Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements. In: First IEEE International Conference on Data Mining, San Jose, CA (2001) · DOI · Paper page
  3. Freund, Y., Schapire, R.: Experiments with a New Boosting Algorithm. In: Proceedings of the 13th International Conference on Machine Learning, pp. 325–332 (1996) · DOI · Paper page
  4. Ting, K.: A Comparative Study of Cost-Sensitive Boosting Algorithms. In: Proceedings of 17 th International Conference on Machine Learning, Stanford, CA, pp. 983–990 (2000)
  5. Fan, W., Stolfo, S., Zhang, J., Chan, P.: AdaCost: Misclassification Cost-Sensitive Boosting. In: Proc. of 16th International Conference on Machine Learning, Slovenia (1999)
  6. Karakoulas, G., Shawe-Taylor, J.: Optimizing Classifiers for Imbalanced Training Sets. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, MIT Press, Cambridge (1999)
  7. Joshi, M., Agarwal, R., Kumar, V.: Predicting Rare Classes: Can Boosting Make Any Weak Learner Strong? In: Proceedings of Eighth ACM Conference ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Edmonton, Canada (2002) · DOI
  8. Joshi, M., Agarwal, R.: PNrule: A New Framework for Learning Classifier Models in Data Mining (A Case-study in Network Intrusion Detection), First SIAM Conference on Data Mining, Chicago, IL (2001) · DOI
  9. Chan, P., Stolfo, S.: Towards Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. In: Proceedings of Fourth ACM Conference ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, pp. 164–168 (1998)
  10. Kubat, M., Holte, R., Matwin, S.: Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning 30, 195–215 (1998) · DOI · Paper page
  11. Japkowicz, N.: The Class Imbalance Problem: Significance and Strategies. In: Proceedings of the 2000 International Conference on Artificial Intelligence (IC-AI 2000): Special Track on Inductive Learning, Las Vegas, Nevada (2000)
  12. Lewis, D., Catlett, J.: Heterogeneous Uncertainty Sampling for Supervised Learning. In: Proceedings of the Eleventh International Conference of Machine Learning, San Francisco, CA, pp. 148–156 (1994) · DOI · Paper page
  13. Ling, C., Li, C.: Data Mining for Direct Marketing Problems and Solutions. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY (1998)
  14. Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Mateo (1992)
  15. Cohen, W.: Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp. 115–123 (1995) · DOI · Paper page
  16. Stanfill, C., Waltz, D.: Toward Memory-based Reasoning. Communications of the ACM 29(12), 1213–1228 (1986) · DOI · Paper page
  17. Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10(1), 57–78 (1993)
  18. KDD-Cup, Task Description (1999), http://kdd.ics.uci.edu/databases/kddcup99/task.html
  19. Lippmann, R., Fried, D., Graf, I., Haines, J., Kendall, K., McClung, D., Weber, D., Webster, S., Wyschogrod, D., Cunningham, R., Zissman, M.: Evaluating Intrusion Detection Systems: The 1998 DARPA Off-line Intrusion Detection Evaluation. In: Proceedings DARPA Information Survivability Conference and Exposition (DISCEX) 2000, vol. 2, pp. 12–26. IEEE Computer Society Press, Los Alamitos (2000) · DOI · Paper page
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Cited by

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Metadata

Authors
Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W.
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2003
Pages
107-119
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-540-39804-2_12
OpenAlex
W1563938718
Cited by
1,668
References
3
FWCI
6.38
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: SMOTEBoost: Improving Prediction of the Minority Class in Boosting. https://app.cochat.ai/papers/10.1007/978-3-540-39804-2_12
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Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W. (2003). SMOTEBoost: Improving Prediction of the Minority Class in Boosting. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-39804-2_12

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