-
1
-
-
77950806386
-
A new performance measure for class imbalance learning: Application to bioinformatics problems
-
Batuwita, R. and Palade, V. (2009). A new performance measure for class imbalance learning: application to bioinformatics problems. In the 8th ICMLA'09, pages 545-550.
-
(2009)
The 8th ICMLA'09
, pp. 545-550
-
-
Batuwita, R.1
Palade, V.2
-
2
-
-
0031191630
-
The use of the area under the ROC curve in the evaluation of machine learning algorithms
-
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145-1159.
-
(1997)
Pattern Recognition
, vol.30
, Issue.7
, pp. 1145-1159
-
-
Bradley, A.P.1
-
3
-
-
33646107181
-
Learning from imbalanced data in surveillance of nosocomial infection
-
Cohen, G., Hilario, M., Sax, H., Hugonnet, S., and Geiss-buhler, A. (2006). Learning from imbalanced data in surveillance of nosocomial infection. Artificial Intelligence Medicine, 37(1):7-18.
-
(2006)
Artificial Intelligence Medicine
, vol.37
, Issue.1
, pp. 7-18
-
-
Cohen, G.1
Hilario, M.2
Sax, H.3
Hugonnet, S.4
Geissbuhler, A.5
-
4
-
-
84973587732
-
A coefficient of agreement for nominal scales
-
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educ and Psychol Meas, 20(1):37-46.
-
(1960)
Educ and Psychol Meas
, vol.20
, Issue.1
, pp. 37-46
-
-
Cohen, J.1
-
5
-
-
33646421421
-
Evaluation of classifiers for an uneven class distribution problem
-
Daskalaki, S., Kopanas, I., and Avouris, N. (2006). Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intelligence, 20(5):381-417.
-
(2006)
Applied Artificial Intelligence
, vol.20
, Issue.5
, pp. 381-417
-
-
Daskalaki, S.1
Kopanas, I.2
Avouris, N.3
-
6
-
-
60649102026
-
Comparison of evaluation metrics in classification applications with imbalanced datasets
-
Fatourechi, M., Ward, R., Mason, S., Huggins, J., Schlogl, A., and Birch, G. (2008). Comparison of evaluation metrics in classification applications with imbalanced datasets. In the 7th ICMLA'08, pages 777-782.
-
(2008)
The 7th ICMLA'08
, pp. 777-782
-
-
Fatourechi, M.1
Ward, R.2
Mason, S.3
Huggins, J.4
Schlogl, A.5
Birch, G.6
-
7
-
-
70349280929
-
An experimental comparison of performance measures for classification
-
Ferri, C., Hernández-Orallo, J., and Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1):27-38.
-
(2009)
Pattern Recognition Letters
, vol.30
, Issue.1
, pp. 27-38
-
-
Ferri, C.1
Hernández-Orallo, J.2
Modroiu, R.3
-
8
-
-
60649086930
-
Comparison of four performance metrics for evaluating sampling techniques for low quality class-imbalanced data
-
Folleco, A., Khoshgoftaar, T. M., and Napolitano, A. (2008). Comparison of four performance metrics for evaluating sampling techniques for low quality class-imbalanced data. In the 7th ICMLA'08, pages 153-158.
-
(2008)
The 7th ICMLA'08
, pp. 153-158
-
-
Folleco, A.1
Khoshgoftaar, T.M.2
Napolitano, A.3
-
9
-
-
78149483936
-
Theoretical analysis of a performance measure for im-balanced data
-
García, V., Mollineda, R. A., and Sánchez, J. S. (2010). Theoretical analysis of a performance measure for im-balanced data. In the 20th ICPR'2010, pages 617-620.
-
(2010)
The 20th ICPR'2010
, pp. 617-620
-
-
García, V.1
Mollineda, R.A.2
Sánchez, J.S.3
-
10
-
-
73349136777
-
Evaluation measures of the classification performance of imbalanced data sets
-
Springer-Verlag
-
Gu, Q., Zhu, L., and Cai, Z. (2009). Evaluation measures of the classification performance of imbalanced data sets. In the 4th ISICA'09, pages 461-471. Springer-Verlag.
-
(2009)
The 4th ISICA'09
, pp. 461-471
-
-
Gu, Q.1
Zhu, L.2
Cai, Z.3
-
11
-
-
14644390912
-
Using AUC and accuracy in evaluating learning algorithms
-
Huang, J. and Ling, C.-X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng, 17(3):299-310.
-
(2005)
IEEE Trans Knowl Data Eng
, vol.17
, Issue.3
, pp. 299-310
-
-
Huang, J.1
Ling, C.-X.2
-
12
-
-
65649085468
-
Constructing new and better evaluation measures for machine learning
-
Huang, J. and Ling, C.-X. (2007). Constructing new and better evaluation measures for machine learning. In the 20th IJCAI'07, pages 859-864.
-
(2007)
The 20th IJCAI'07
, pp. 859-864
-
-
Huang, J.1
Ling, C.-X.2
-
13
-
-
68249123700
-
The impact of gene selection on imbalanced microarray expression data
-
Kamal, A., Zhu, X., Pandya, A., Hsu, S., and Shoaib, M. (2009). The impact of gene selection on imbalanced microarray expression data. In the 1st BICoB'09, pages 259-269.
-
(2009)
The 1st BICoB'09
, pp. 259-269
-
-
Kamal, A.1
Zhu, X.2
Pandya, A.3
Hsu, S.4
Shoaib, M.5
-
14
-
-
78650096173
-
Learning without default: A study of one-class classification and the low-default portfolio problem
-
Kennedy, K., Mac Namee, B., and Delany, S. (2010). Learning without default: A study of one-class classification and the low-default portfolio problem. In the AICS'09, pages 174-187.
-
(2010)
The AICS'09
, pp. 174-187
-
-
Kennedy, K.1
Mac Namee, B.2
Delany, S.3
-
15
-
-
79960872876
-
Predicting disease risks from highly imbalanced data using random forest
-
Khalilia, M., Chakraborty, S., and Popescu, M. (2011). Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making, 11(1):51.
-
(2011)
BMC Medical Informatics and Decision Making
, vol.11
, Issue.1
, pp. 51
-
-
Khalilia, M.1
Chakraborty, S.2
Popescu, M.3
-
16
-
-
0001972236
-
Addressing the curse of imbalanced training sets: One-sided selection
-
Kubat, M. and Matwin, S. (1997). Addressing the curse of imbalanced training sets: one-sided selection. In 14th ICML, pages 179-186.
-
(1997)
14th ICML
, pp. 179-186
-
-
Kubat, M.1
Matwin, S.2
-
17
-
-
34547372256
-
Optimized precision - A new measure for classifier performance evaluation
-
Ranawana, R. and Palade, V. (2006). Optimized Precision - a new measure for classifier performance evaluation. In the IEEE CEC'09, pages 2254-2261.
-
(2006)
The IEEE CEC'09
, pp. 2254-2261
-
-
Ranawana, R.1
Palade, V.2
-
19
-
-
77949533117
-
A study on the relationships of classifier performance metrics
-
Seliya, N., Khoshgoftaar, T., and Van Hulse, J. (2009). A study on the relationships of classifier performance metrics. In the 21st ICTAI'09, pages 59-66.
-
(2009)
The 21st ICTAI'09
, pp. 59-66
-
-
Seliya, N.1
Khoshgoftaar, T.2
Van Hulse, J.3
-
20
-
-
51849156137
-
Beyond accuracy, f-score and roc: A family of discriminant measures for performance evaluation
-
Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond accuracy, f-score and roc: A family of discriminant measures for performance evaluation. In the AJ-CAI'06, pages 1015-1021.
-
(2006)
The AJ-CAI'06
, pp. 1015-1021
-
-
Sokolova, M.1
Japkowicz, N.2
Szpakowicz, S.3
-
21
-
-
65649138430
-
A systematic analysis of performance measures for classification tasks
-
Sokolova, M. and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Inf Process & Manag, 45(4):427-437.
-
(2009)
Inf Process & Manag
, vol.45
, Issue.4
, pp. 427-437
-
-
Sokolova, M.1
Lapalme, G.2
-
22
-
-
67650706774
-
Classification of imbalanced data: A review
-
Sun, Y., Wong, A., and Kamel, M. S. (2009). Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 23(4):687-719.
-
(2009)
International Journal of Pattern Recognition and Artificial Intelligence
, vol.23
, Issue.4
, pp. 687-719
-
-
Sun, Y.1
Wong, A.2
Kamel, M.S.3
-
23
-
-
0242625291
-
Selecting the right interestingness measure for association patterns
-
Tan, P.-N., Kumar, V., and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In the 8th ACM SIGKDD'02, pages 32-41.
-
(2002)
The 8th ACM SIGKDD'02
, pp. 32-41
-
-
Tan, P.-N.1
Kumar, V.2
Srivastava, J.3
-
24
-
-
84870568797
-
A new evaluation measure for imbalanced datasets
-
Weng, C. G. and Poon, J. (2008). A new evaluation measure for imbalanced datasets. In the 7th AusDM'08, pages 27-32.
-
(2008)
The 7th AusDM'08
, pp. 27-32
-
-
Weng, C.G.1
Poon, J.2
|