-
1
-
-
0003408496
-
-
Department of Information and Computer Science, University of California, Irvine, CA
-
C. Blake, E. Keogh, and C. J. Merz. UCI repository of machine learning databases, [http://www.ics.uci.edu/~mlearn/MLRepository.html], Department of Information and Computer Science, University of California, Irvine, CA, 1998.
-
(1998)
UCI repository of machine learning databases
-
-
Blake, C.1
Keogh, E.2
Merz, C.J.3
-
2
-
-
85083464467
-
Toward scalable learning with nonuniform class and cost distributions: A case study in credit card fraud detection
-
New York, NY
-
P. Chan and S. Stolfo. Toward scalable learning with nonuniform class and cost distributions: A case study in credit card fraud detection. In Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining, pages 164-168, New York, NY, 1998.
-
(1998)
Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining
, pp. 164-168
-
-
Chan, P.1
Stolfo, S.2
-
3
-
-
27144549260
-
Editorial to the special issue on learning from imbalanced data sets
-
61, 6
-
N. V. Chawla, N. Japkowicz, and A. Kotcz. Editorial to the special issue on learning from imbalanced data sets. ACM SIGKDD Explorations, 6(1): -6, 2004.
-
(2004)
ACM SIGKDD Explorations
-
-
Chawla, N.V.1
Japkowicz, N.2
Kotcz, A.3
-
4
-
-
77953601237
-
Improving classifier utility by altering the misclassification cost ratio
-
Chicago, IL
-
M. Ciraco, M. Rogalewski, and G. Weiss. Improving classifier utility by altering the misclassification cost ratio. In Proceedings of the 1st International Workshop on Utility-Based Data Mining, pages 46-52, Chicago, IL, 2005.
-
(2005)
Proceedings of the 1st International Workshop on Utility-Based Data Mining
, pp. 46-52
-
-
Ciraco, M.1
Rogalewski, M.2
Weiss, G.3
-
6
-
-
31344458519
-
C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling
-
Washington, DC
-
C. Drummond and R. C. Holte. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Working Notes of the ICML'03 Workshop on Learning from Imbalanced Data Sets, Washington, DC, 2003.
-
(2003)
Working Notes of the ICML'03 Workshop on Learning from Imbalanced Data Sets
-
-
Drummond, C.1
Holte, R.C.2
-
10
-
-
0036565589
-
An instance-weighting method to induce costsensitive trees
-
K. M. Ting. An instance-weighting method to induce costsensitive trees. IEEE Transactions on Knowledge and Data Engineering, 14(3):659-665, 2002.
-
(2002)
IEEE Transactions on Knowledge and Data Engineering
, vol.14
, Issue.3
, pp. 659-665
-
-
Ting, K.M.1
-
11
-
-
1442275185
-
Learning when training data are costly: The effect of class distribution on tree induction
-
G. Weiss and F. Provost. Learning when training data are costly: The effect of class distribution on tree induction. Joural of Artificial Intelligence Research, 19:315-354, 2003.
-
(2003)
Joural of Artificial Intelligence Research
, vol.19
, pp. 315-354
-
-
Weiss, G.1
Provost, F.2
-
12
-
-
20844458491
-
Mining with rarity - problems and solutions: A unifying framework
-
G. M. Weiss. Mining with rarity - problems and solutions: A unifying framework. SIGKDD Explorations, 6(1):7-19, 2004.
-
(2004)
SIGKDD Explorations
, vol.6
, Issue.1
, pp. 7-19
-
-
Weiss, G.M.1
-
13
-
-
33749245586
-
Cost-sensitive learning by cost-proportionate example weighting
-
Melbourne, FL
-
B. Zadrozny, J. Langford, and N. Abe. Cost-sensitive learning by cost-proportionate example weighting. In Proceedings of the 3rd IEEE International Conference on Data Mining, pages 435-442, Melbourne, FL, 2003.
-
(2003)
Proceedings of the 3rd IEEE International Conference on Data Mining
, pp. 435-442
-
-
Zadrozny, B.1
Langford, J.2
Abe, N.3
-
15
-
-
31344442851
-
Training cost-sensitive neural networks with methods addressing the class imbalance problem
-
Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18(1):63-77, 2006.
-
(2006)
IEEE Transactions on Knowledge and Data Engineering
, vol.18
, Issue.1
, pp. 63-77
-
-
Zhou, Z.-H.1
Liu, X.-Y.2
|