-
1
-
-
84937523920
-
Random balance: Ensembles of variable priors classifiers for imbalanced data
-
Díez-Pastor J.F., Rodríguez J.J., García-Osorio C. et al. Random balance: ensembles of variable priors classifiers for imbalanced data Knowl.-Based Syst. 85 2015 96 111
-
(2015)
Knowl.-Based Syst.
, vol.85
, pp. 96-111
-
-
Díez-Pastor, J.F.1
Rodríguez, J.J.2
García-Osorio, C.3
-
2
-
-
84881072864
-
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
-
M. Galar, A. Fernández, E. Barrenechea EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling Pattern Recogn. 46 2013 3460 3471
-
(2013)
Pattern Recogn.
, vol.46
, pp. 3460-3471
-
-
Galar, M.1
Fernández, A.2
Barrenechea, E.3
-
3
-
-
84921279740
-
Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms
-
P. Vorraboot, S. Rasmequan, K. Chinnasarn Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms Neurocomputing 152 2015 429 443
-
(2015)
Neurocomputing
, vol.152
, pp. 429-443
-
-
Vorraboot, P.1
Rasmequan, S.2
Chinnasarn, K.3
-
4
-
-
84921817000
-
A novel ensemble method for classifying imbalanced data
-
Z. Sun, Q. Song, X. Zhu A novel ensemble method for classifying imbalanced data Pattern Recogn. 48 2015 1623 1637
-
(2015)
Pattern Recogn.
, vol.48
, pp. 1623-1637
-
-
Sun, Z.1
Song, Q.2
Zhu, X.3
-
6
-
-
84891860723
-
Training and assessing classification rules with imbalanced data
-
G. Menardi, N. Torelli Training and assessing classification rules with imbalanced data Data Min. Knowl. Discov. 2014 92 122
-
(2014)
Data Min. Knowl. Discov.
, pp. 92-122
-
-
Menardi, G.1
Torelli, N.2
-
7
-
-
84906691312
-
Feature selection for high-dimensional class-imbalanced data sets using support vector machines
-
S. Maldonado, R. Weber, F. Famili Feature selection for high-dimensional class-imbalanced data sets using support vector machines Inf. Sci. 286 2014 228 246
-
(2014)
Inf. Sci.
, vol.286
, pp. 228-246
-
-
Maldonado, S.1
Weber, R.2
Famili, F.3
-
8
-
-
52149095977
-
Application of fuzzy closeness degree in reservoir recognition
-
G.Q. Feng, Y. Li, D.H. Tan Application of fuzzy closeness degree in reservoir recognition J. Southwest Pet. Inst. 21 4 1999 46 49
-
(1999)
J. Southwest Pet. Inst.
, vol.21
, Issue.4
, pp. 46-49
-
-
Feng, G.Q.1
Li, Y.2
Tan, D.H.3
-
9
-
-
77957900643
-
Optimizing reservoir features in oil exploration management based on fusion of soft computing
-
Guo H.X., Liao X.W., Zhu K.J. Optimizing reservoir features in oil exploration management based on fusion of soft computing Appl. Soft Comput. 11 2011 1144 1155
-
(2011)
Appl. Soft Comput.
, vol.11
, pp. 1144-1155
-
-
Guo, H.X.1
Liao, X.W.2
Zhu, K.J.3
-
11
-
-
84921279740
-
Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms
-
P. Vorraboot, S. Rasmequan, K. Chinnasarn Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms Neurocomputing 152 2015 429 443
-
(2015)
Neurocomputing
, vol.152
, pp. 429-443
-
-
Vorraboot, P.1
Rasmequan, S.2
Chinnasarn, K.3
-
12
-
-
84937523920
-
Random balance: Ensembles of variable priors classifiers for imbalanced data
-
Díez-Pastor J.F., Rodríguez J.J., García-Osorio C. et al. Random balance: ensembles of variable priors classifiers for imbalanced data Knowl.-Based Syst. 85 2015 96 111
-
(2015)
Knowl.-Based Syst.
, vol.85
, pp. 96-111
-
-
Díez-Pastor, J.F.1
Rodríguez, J.J.2
García-Osorio, C.3
-
13
-
-
58149321460
-
Boosting a weak learning algorithm by majority
-
Yoav Freund Boosting a weak learning algorithm by majority Inf. Comput. 121 2 1995 256 285
-
(1995)
Inf. Comput.
, vol.121
, Issue.2
, pp. 256-285
-
-
Yoav, F.1
-
14
-
-
84926016915
-
A new approach for imbalanced data classification based on data gravitation
-
Peng L., Zhang H., Yang B. et al. A new approach for imbalanced data classification based on data gravitation Inf. Sci. 288 2014 347 373
-
(2014)
Inf. Sci.
, vol.288
, pp. 347-373
-
-
Peng, L.1
Zhang, H.2
Yang, B.3
-
15
-
-
84921817000
-
A novel ensemble method for classifying imbalanced data
-
Z. Sun, Q. Song, X. Zhu A novel ensemble method for classifying imbalanced data Pattern Recogn. 48 2015 1623 1637
-
(2015)
Pattern Recogn.
, vol.48
, pp. 1623-1637
-
-
Sun, Z.1
Song, Q.2
Zhu, X.3
-
17
-
-
84911445875
-
Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data
-
V. López, S. Río, J.M. Benítez et al. Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data Fuzzy Sets Syst. 258 2015 5 38
-
(2015)
Fuzzy Sets Syst.
, vol.258
, pp. 5-38
-
-
López, V.1
Río, S.2
Benítez, J.M.3
-
18
-
-
84889092504
-
Cost-sensitive decision tree ensembles for effective imbalanced classification
-
B. Krawczyk, M. Wozniak, G. Schaefer Cost-sensitive decision tree ensembles for effective imbalanced classification Appl. Soft Comput. 14 2014 554 562
-
(2014)
Appl. Soft Comput.
, vol.14
, pp. 554-562
-
-
Krawczyk, B.1
Wozniak, M.2
Schaefer, G.3
-
19
-
-
84937927040
-
Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs
-
Datta S., Das S. Near-bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs Neural Netw. 70 2015 39 52
-
(2015)
Neural Netw.
, vol.70
, pp. 39-52
-
-
Datta, S.1
Das, S.2
-
20
-
-
84875404700
-
Feature selection for high-dimensional imbalanced data
-
L. Yin, Y. Ge, K. Xiao Feature selection for high-dimensional imbalanced data Neurocomputing 105 2013 3 11
-
(2013)
Neurocomputing
, vol.105
, pp. 3-11
-
-
Yin, L.1
Ge, Y.2
Xiao, K.3
-
21
-
-
84867580242
-
DBFS: An effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets
-
M. Alibeigi, S. Hashemi, A. Hamzeh DBFS: an effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets Data Knowl. Eng. 81-82 4 2012 67 103
-
(2012)
Data Knowl. Eng.
, vol.81-82
, Issue.4
, pp. 67-103
-
-
Alibeigi, M.1
Hashemi, S.2
Hamzeh, A.3
-
23
-
-
84930483289
-
Using Voronoi diagrams to improve classification performances when modeling imbalanced datasets
-
Ii W.A.Y., Nykl S.L., Weckman G.R. et al. Using Voronoi diagrams to improve classification performances when modeling imbalanced datasets Neural Comput. Appl. 26 2015 1 14
-
(2015)
Neural Comput. Appl.
, vol.26
, pp. 1-14
-
-
Ii, W.A.Y.1
Nykl, S.L.2
Weckman, G.R.3
-
25
-
-
64049108468
-
Exploratory Under-sampling for class-imbalance learning, bioinformatics
-
X.Y. Liu, J. Wu, Z.H. Zhou Exploratory Under-sampling for class-imbalance learning, bioinformatics Proceedings of the IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2) 2009 539 550
-
(2009)
Proceedings of the IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
, vol.392
, pp. 539-550
-
-
Liu, X.Y.1
Wu, J.2
Zhou, Z.H.3
-
26
-
-
84868621497
-
ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data
-
H. Yu, J. Ni, J. Zhao ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data Neurocomputing 101 2013 309 318
-
(2013)
Neurocomputing
, vol.101
, pp. 309-318
-
-
Yu, H.1
Ni, J.2
Zhao, J.3
-
28
-
-
84883447718
-
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
-
V. López, A. Fernández, S. García et al. An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics Inf. Sci. 250 2013 113 141
-
(2013)
Inf. Sci.
, vol.250
, pp. 113-141
-
-
López, V.1
Fernández, A.2
García, S.3
-
30
-
-
33646054819
-
Multi-class ROC analysis from a multi-objective optimization perspective
-
Richard M., Jonathan E. Multi-class ROC analysis from a multi-objective optimization perspective Pattern Recogn. Lett. 27 2006 916 927
-
(2006)
Pattern Recogn. Lett.
, vol.27
, pp. 916-927
-
-
Richard, M.1
Jonathan, E.2
-
33
-
-
84857049821
-
A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior
-
W. Bin, P. Qinke, Z. Jing A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior Eur. J. Oper. Res. 219 2012 224 233
-
(2012)
Eur. J. Oper. Res.
, vol.219
, pp. 224-233
-
-
Bin, W.1
Qinke, P.2
Jing, Z.3
-
36
-
-
85009220341
-
Classifying imbalanced data in distance-based feature space
-
Ando S. Classifying imbalanced data in distance-based feature space Knowl. Inf. Syst. 2015 1 24
-
(2015)
Knowl. Inf. Syst.
, pp. 1-24
-
-
Ando, S.1
-
37
-
-
84942249246
-
Class imbalance revisited: A new experimental setup to assess the performance of treatment methods
-
R.C. Prati, Gustavo E., DiegoF. Silva Class imbalance revisited: a new experimental setup to assess the performance of treatment methods Knowl. Inf. Syst. 45 2015 247 270
-
(2015)
Knowl. Inf. Syst.
, vol.45
, pp. 247-270
-
-
Prati, R.C.1
Gustavo, E.2
Silva DiegoF.3
-
38
-
-
84867863746
-
Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings
-
Liu Z., Cao H., Chen X. et al. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings Neurocomputing 99 1 2013 399 410
-
(2013)
Neurocomputing
, vol.99
, Issue.1
, pp. 399-410
-
-
Liu, Z.1
Cao, H.2
Chen, X.3
-
40
-
-
84874667219
-
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
-
Alberto F., Victoria L., Mikel G. et al. Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches Knowl.-Based Syst. 42 2013 97 110
-
(2013)
Knowl.-Based Syst.
, vol.42
, pp. 97-110
-
-
Alberto, F.1
Victoria, L.2
Mikel, G.3
-
41
-
-
0001884644
-
Individual comparisons by ranking methods
-
F. Wilcoxon Individual comparisons by ranking methods Biometr. Bull. 6 1945 80 83
-
(1945)
Biometr. Bull.
, vol.6
, pp. 80-83
-
-
Wilcoxon, F.1
-
43
-
-
0002294347
-
A simple sequentially rejective multiple test procedure
-
S. Holm A simple sequentially rejective multiple test procedure Scand. J. Stat. 6 1979 65 70
-
(1979)
Scand. J. Stat.
, vol.6
, pp. 65-70
-
-
Holm, S.1
-
44
-
-
84907532927
-
Training algorithms for radial basis function networks to tackle learning processes with imbalanced data-sets
-
M.D. Pérez-Godoy, Rivera A.J, Carmona C.J. et al. Training algorithms for radial basis function networks to tackle learning processes with imbalanced data-sets Appl. Soft Comput. 25 2014 26 39
-
(2014)
Appl. Soft Comput.
, vol.25
, pp. 26-39
-
-
Pérez-Godoy, M.D.1
Rivera, A.J.2
Carmona, C.J.3
-
45
-
-
0033281701
-
Improved boosting algorithms using confidence-rated pre-dictions
-
RobertE. Schapire, Yoram Singer Improved boosting algorithms using confidence-rated pre-dictions Mach. Learn. 37 3 1999 297 336
-
(1999)
Mach. Learn.
, vol.37
, Issue.3
, pp. 297-336
-
-
Schapire RobertE.1
Singer, Y.2
-
46
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application to boosting
-
Yoav Freund, RobertE. Schapire A decision-theoretic generalization of on-line learning and an application to boosting J. Comput. Syst. Sci. 55 1 1997 119 139
-
(1997)
J. Comput. Syst. Sci.
, vol.55
, Issue.1
, pp. 119-139
-
-
Freund, Y.1
Schapire RobertE.2
-
48
-
-
84899475632
-
A novel selective ensemble algorithm for imbalanced data classification based on exploratory undersampling
-
Yin Q.Y., Zhang J.S., Zhang C.X. et al. A novel selective ensemble algorithm for imbalanced data classification based on exploratory undersampling Math. Prob. Eng. 71 3 2014 741 764
-
(2014)
Math. Prob. Eng.
, vol.71
, Issue.3
, pp. 741-764
-
-
Yin, Q.Y.1
Zhang, J.S.2
Zhang, C.X.3
-
49
-
-
84875404700
-
Feature selection for high-dimensional imbalanced data
-
L. Yin, Y. Ge, K. Xiao Feature selection for high-dimensional imbalanced data Neurocomputing 105 3 2013 3 11
-
(2013)
Neurocomputing
, vol.105
, Issue.3
, pp. 3-11
-
-
Yin, L.1
Ge, Y.2
Xiao, K.3
-
52
-
-
84921279740
-
Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms
-
Vorraboot P., Rasmequan S., Lursinsap C. et al. Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms Neurocomputing 152 2015 429 443
-
(2015)
Neurocomputing
, vol.152
, pp. 429-443
-
-
Vorraboot, P.1
Rasmequan, S.2
Lursinsap, C.3
-
54
-
-
79951771270
-
Imbalanced classification using support vector machine ensemble
-
Tian J., Gu H., Liu W. Imbalanced classification using support vector machine ensemble Neural Comput. Appl. 20 2 2011 203 209
-
(2011)
Neural Comput. Appl.
, vol.20
, Issue.2
, pp. 203-209
-
-
Tian, J.1
Gu, H.2
Liu, W.3
-
56
-
-
84953640240
-
Boosted SVM with active learning strategy for imbalanced data
-
Zięba M., Tomczak J.M. Boosted SVM with active learning strategy for imbalanced data Soft Comput. 2014 1 12
-
(2014)
Soft Comput.
, pp. 1-12
-
-
Zięba, M.1
Tomczak, J.M.2
-
58
-
-
84908127920
-
Improving class probability estimates for imbalanced data
-
Wallace B.C., Dahabreh I.J. Improving class probability estimates for imbalanced data Knowl. Inf. Syst. 41 1 2014 33 52
-
(2014)
Knowl. Inf. Syst.
, vol.41
, Issue.1
, pp. 33-52
-
-
Wallace, B.C.1
Dahabreh, I.J.2
-
59
-
-
84888645340
-
On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed
-
López V., Fernández A., Herrera F. On the importance of the validation technique for classification with imbalanced datasets: addressing covariate shift when data is skewed Inf. Sci. 257 2 2014 1 13
-
(2014)
Inf. Sci.
, vol.257
, Issue.2
, pp. 1-13
-
-
López, V.1
Fernández, A.2
Herrera, F.3
-
63
-
-
84926525100
-
Coupling different methods for overcoming the class imbalance problem
-
Nanni L., Fantozzi C., &Lazzarini N. Coupling different methods for overcoming the class imbalance problem Neurocomputing 158 2015 48 61
-
(2015)
Neurocomputing
, vol.158
, pp. 48-61
-
-
Nanni, L.1
Fantozzi, C.2
Lazzarini, N.3
-
64
-
-
84893196110
-
ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography
-
Song B., Zhang G., Wei Z. et al. ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography Int. J. Comput. Assist. Radiol. Surg. 9 1 2014 79 89
-
(2014)
Int. J. Comput. Assist. Radiol. Surg.
, vol.9
, Issue.1
, pp. 79-89
-
-
Song, B.1
Zhang, G.2
Wei, Z.3
-
65
-
-
84942363608
-
Addressing imbalanced data with argument based rule learning
-
Jerzy S., Krystyna N. Addressing imbalanced data with argument based rule learning Exp. Syst. Appl. 24 2015 9468 9481
-
(2015)
Exp. Syst. Appl.
, vol.24
, pp. 9468-9481
-
-
Jerzy, S.1
Krystyna, N.2
-
66
-
-
25144492516
-
Efficient feature selection via analysis of relvance and redundancy
-
Yu L., Liu H., Yu L. et al. Efficient feature selection via analysis of relvance and redundancy J. Mach. Learn. Res. 5 4 2004 1205 1224
-
(2004)
J. Mach. Learn. Res.
, vol.5
, Issue.4
, pp. 1205-1224
-
-
Yu, L.1
Liu, H.2
Yu, L.3
-
68
-
-
33750695043
-
A novel classification method based on data gravitation, neural networks and brain, 2005
-
Peng L., Chen Y., Yang B. et al. A novel classification method based on data gravitation, neural networks and brain, 2005 ICNN&B '05. International Conference on IEEE 2005 667 672
-
(2005)
ICNN&B '05. International Conference on IEEE
, pp. 667-672
-
-
Peng, L.1
Chen, Y.2
Yang, B.3
|