-
1
-
-
27144531570
-
A study of the behavior of several methods for balancing machine learning training data
-
G. Batista, R. C., Prati, and M. C., Monard, "A study of the behavior of several methods for balancing machine learning training data," SIGKDD Explorations, vol. 6, pp. 20-29, 2004.
-
(2004)
SIGKDD Explorations
, vol.6
, pp. 20-29
-
-
Batista, G.1
Prati, R.C.2
Monard, M.C.3
-
2
-
-
27144549260
-
Editorial: Special issue on learning from imbalanced data sets
-
N. V. Chawla, N., Japkowicz, & A., Kolcz, "Editorial: Special issue on learning from imbalanced data sets," SIGKDD Explorations, vol. 6, p. 16, 2004.
-
(2004)
SIGKDD Explorations
, vol.6
, pp. 16
-
-
Chawla, N.V.1
Japkowicz, N.2
Kolcz, A.3
-
3
-
-
27144479454
-
Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach
-
H. Guo, and H. L., Viktor, "Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach," SIGKDD Explorations, vol. 6, pp. 30-39, 2004.
-
(2004)
SIGKDD Explorations
, vol.6
, pp. 30-39
-
-
Guo, H.1
Viktor, H.L.2
-
5
-
-
5044220135
-
Learning classifiers from imbalanced data based on biased minimax probability machine
-
K. Huang, H., Yang, I., King, and M., Lyu, "Learning classifiers from imbalanced data based on biased minimax probability machine," in Proceedings of the 04' IEEE Computer Society conference on computer vision and pattern recognition (CVPR'04), pp. 558-563.
-
Proceedings of the 04' IEEE Computer Society conference on computer vision and pattern recognition (CVPR'04)
, pp. 558-563
-
-
Huang, K.1
Yang, H.2
King, I.3
Lyu, M.4
-
6
-
-
27144540575
-
Class imbalances versus small disjuncts
-
T. Jo, and N., Japkowicz, "Class imbalances versus small disjuncts," SIGKDD Explorations, vol. 6, pp. 40-49, 2004.
-
(2004)
SIGKDD Explorations
, vol.6
, pp. 40-49
-
-
Jo, T.1
Japkowicz, N.2
-
7
-
-
5044220135
-
Learning classifiers from imbalanced data based on biased minimax probability machine
-
K. Huang, H., Yang, I., King, and M, Lyu, "Learning classifiers from imbalanced data based on biased minimax probability machine," in Proceedings of the 04' IEEE Computer Society conference on computer vision and pattern recognition (CVPR '04), 2004, pp. 558-563.
-
(2004)
Proceedings of the 04' IEEE Computer Society conference on computer vision and pattern recognition (CVPR '04)
, pp. 558-563
-
-
Huang, K.1
Yang, H.2
King, I.3
Lyu, M.4
-
8
-
-
33744797928
-
Knowledge acquisition through information granulation for imbalanced data
-
C.T., Su, L.S., Chen, Y. Yih, "Knowledge acquisition through information granulation for imbalanced data", Expert Systems with Applications, vol. 31, pp. 531-541, 2006.
-
(2006)
Expert Systems with Applications
, vol.31
, pp. 531-541
-
-
Su, C.T.1
Chen, L.S.2
Yih, Y.3
-
10
-
-
0035163419
-
On Modeling data mining with granular computing
-
Chicago
-
Y. Y. Yao, "On Modeling data mining with granular computing," in Proceeding of COMPSAC 2001, Chicago, 2001, pp. 638-643.
-
(2001)
Proceeding of COMPSAC 2001
, pp. 638-643
-
-
Yao, Y.Y.1
-
11
-
-
0035792842
-
Information granulation via neural network-based learning
-
G. Castellano, and A. M, Fanelli, "Information granulation via neural network-based learning," in IFSA World Congress and 20th NAFIPS international conference. vol. 5, 2001, pp. 3059-3064.
-
(2001)
IFSA World Congress and 20th NAFIPS international conference
, vol.5
, pp. 3059-3064
-
-
Castellano, G.1
Fanelli, A.M.2
-
13
-
-
0346586663
-
SMOTE: Synthetic minority oversampling technique
-
N. V., Chawla, K., Bowyer, L., Hall, and W., Kegelmeyer, "SMOTE: Synthetic minority oversampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 231-357, 2002.
-
(2002)
Journal of Artificial Intelligence Research
, vol.16
, pp. 231-357
-
-
Chawla, N.V.1
Bowyer, K.2
Hall, L.3
Kegelmeyer, W.4
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