-
1
-
-
84921669899
-
Imbalance learning method using subset filtering
-
K.N. Rao, T.V. Rao, D.R. Lakshmi, and A. Novel Class Imbalance learning method using subset filtering Int. J. Sci. Eng. Res. 3 9 2012 1 9
-
(2012)
Int. J. Sci. Eng. Res.
, vol.3
, Issue.9
, pp. 1-9
-
-
Rao, K.N.1
Rao, T.V.2
Lakshmi, D.R.3
Novel Class, A.4
-
2
-
-
84881072864
-
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
-
M. Galar, A. Fernandez, E. Barrenechea, and F. Herrera EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling Pattern Recognit. 46 2013 3460 3471
-
(2013)
Pattern Recognit.
, vol.46
, pp. 3460-3471
-
-
Galar, M.1
Fernandez, A.2
Barrenechea, E.3
Herrera, F.4
-
4
-
-
70349617264
-
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
-
S. García, and F. Herrera Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy Evol. Comput. 17 2009 275 306
-
(2009)
Evol. Comput.
, vol.17
, pp. 275-306
-
-
García, S.1
Herrera, F.2
-
5
-
-
61549114384
-
SVMs modeling for highly imbalanced classification
-
Y. Tang, Y.Q. Zhang, N.V. Chawla, and S. Krasser SVMs modeling for highly imbalanced classification IEEE Trans. Syst. Man Cybern. Part B 39 1 2009 281 288
-
(2009)
IEEE Trans. Syst. Man Cybern. Part B
, vol.39
, Issue.1
, pp. 281-288
-
-
Tang, Y.1
Zhang, Y.Q.2
Chawla, N.V.3
Krasser, S.4
-
7
-
-
33845536164
-
The class imbalance problem: A systematic study
-
N. Japkowicz, and S. Stephen The class imbalance problem: a systematic study Intell. Data Anal. 6 5 2002 429 449
-
(2002)
Intell. Data Anal.
, vol.6
, Issue.5
, pp. 429-449
-
-
Japkowicz, N.1
Stephen, S.2
-
9
-
-
84857022489
-
Adjusted geometric-mean: A novel performance measure for imbalanced bioinformatics dataset learning
-
R. Batuwita, and V. Palade Adjusted geometric-mean: a novel performance measure for imbalanced bioinformatics dataset learning J. Bioinform. Comput. Biol. 10 4 2012 1 23
-
(2012)
J. Bioinform. Comput. Biol.
, vol.10
, Issue.4
, pp. 1-23
-
-
Batuwita, R.1
Palade, V.2
-
10
-
-
14644390912
-
Using AUC and accuracy in evaluating learning algorithms
-
J. Huang, and C.X. Ling Using AUC and accuracy in evaluating learning algorithms IEEE Trans. Knowl. Data Eng. 17 3 2005 299 310
-
(2005)
IEEE Trans. Knowl. Data Eng.
, vol.17
, Issue.3
, pp. 299-310
-
-
Huang, J.1
Ling, C.X.2
-
12
-
-
11244308266
-
Class-boundary alignment for imbalanced dataset learning
-
G. Wu, E.Y. Chang, Class-boundary alignment for imbalanced dataset learning, in: Proceedings of International Conference on Machine Learning 2003 Workshop on Learning from Imbalanced Data Sets II, Washington, DC, 2003.
-
(2003)
Proceedings of International Conference on Machine Learning 2003 Workshop on Learning from Imbalanced Data Sets II, Washington, DC
-
-
Wu, G.1
Chang, E.Y.2
-
13
-
-
85041528332
-
Reducing misclassification costs
-
M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, C. Brunk, Reducing misclassification costs, in: Proceedings of the 11th International Conference on Machine Learning, 1994, pp. 217-225.
-
(1994)
Proceedings of the 11th International Conference on Machine Learning
, pp. 217-225
-
-
Pazzani, M.1
Merz, C.2
Murphy, P.3
Ali, K.4
Hume, T.5
Brunk, C.6
-
17
-
-
84862515469
-
A review on ensembles for the class imbalance problem: Bagging, boosting and hybrid-based approaches
-
M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera A review on ensembles for the class imbalance problem: bagging, boosting and hybrid-based approaches IEEE Trans. Syst. Man. Cybern. Part C: Appl. Rev. 42 4 2012 463 484
-
(2012)
IEEE Trans. Syst. Man. Cybern. Part C: Appl. Rev.
, vol.42
, Issue.4
, pp. 463-484
-
-
Galar, M.1
Fernandez, A.2
Barrenechea, E.3
Bustince, H.4
Herrera, F.5
-
18
-
-
0030211964
-
Bagging predictors
-
L. Breiman Bagging predictors Mach. Learn. 24 1996 123 140
-
(1996)
Mach. Learn.
, vol.24
, pp. 123-140
-
-
Breiman, L.1
-
19
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application to boosting
-
Y. Freund, and R.E. 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, R.E.2
-
20
-
-
9444297357
-
Smoteboost: Improving prediction of the minority class in boosting
-
N.V. Chawla, A. Lazarevic, L.O. Hall, K.W. Bowyer, Smoteboost: improving prediction of the minority class in boosting, in: Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, 2003, pp. 107-119.
-
(2003)
Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases
, pp. 107-119
-
-
Chawla, N.V.1
Lazarevic, A.2
Hall, L.O.3
Bowyer, K.W.4
-
22
-
-
15944382343
-
Integrating support vector machines in a hierarchical output space decomposition framework
-
Y. Chen, M.M. Crawford, J. Ghosh, Integrating support vector machines in a hierarchical output space decomposition framework, in: Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, vol. 2, 2004, pp. 949-952.
-
(2004)
Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing, Vol. 2
, pp. 949-952
-
-
Chen, Y.1
Crawford, M.M.2
Ghosh, J.3
-
23
-
-
20444392475
-
Using random forest to learn imbalanced data
-
(Available at:) Statistics Department, University of California Berkeley
-
C. Chen, A. Liaw, and L. Breiman Using random forest to learn imbalanced data (Available at:) Technical Report 666 2004 Statistics Department, University of California Berkeley
-
(2004)
Technical Report 666
-
-
Chen, C.1
Liaw, A.2
Breiman, L.3
-
24
-
-
84872396713
-
Species distribution modeling and prediction: A class imbalance problem
-
R.A. Johnson, N.V. Chawla, J.J. Hellman, Species distribution modeling and prediction: a class imbalance problem, in: Proceedings of Intelligent Data Understanding (CIDU), 2012, pp. 9-16.
-
(2012)
Proceedings of Intelligent Data Understanding (CIDU)
, pp. 9-16
-
-
Johnson, R.A.1
Chawla, N.V.2
Hellman, J.J.3
-
25
-
-
27144501672
-
Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning
-
H. Han, W.Y. Wang, and B.H. Mao Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning Adv. Intell. Comput. 2005 878 887
-
(2005)
Adv. Intell. Comput.
, pp. 878-887
-
-
Han, H.1
Wang, W.Y.2
Mao, B.H.3
-
26
-
-
77950231896
-
MSMOTE: Improving classification performance when training data is imbalanced
-
S. Hu, Y. Liang, L. Ma, Y. He, MSMOTE: improving classification performance when training data is imbalanced, in: Proceedings of 2nd International Workshop on Computer Science Engineering, vol. 2, 2009, pp. 13-17.
-
(2009)
Proceedings of 2nd International Workshop on Computer Science Engineering
, vol.2
, pp. 13-17
-
-
Hu, S.1
Liang, Y.2
Ma, L.3
He, Y.4
-
27
-
-
84891807032
-
MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning
-
S. Barua, M. Islam, X. Yao, and K. Murase MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning IEEE Trans. Knowl. Data Eng. 26 2 2014 405 425
-
(2014)
IEEE Trans. Knowl. Data Eng.
, vol.26
, Issue.2
, pp. 405-425
-
-
Barua, S.1
Islam, M.2
Yao, X.3
Murase, K.4
-
28
-
-
4744344959
-
Classification and knowledge discovery in protein databases
-
P. Radivojac, N.V. Chawla, K. Dunker, and Z. Obradovic Classification and knowledge discovery in protein databases J. Biomed. Inform. 37 4 2004 224 239
-
(2004)
J. Biomed. Inform.
, vol.37
, Issue.4
, pp. 224-239
-
-
Radivojac, P.1
Chawla, N.V.2
Dunker, K.3
Obradovic, Z.4
-
31
-
-
44649170363
-
Boosting support vector machines for imbalanced data sets
-
B.X. Wang, and N. Japkowicz Boosting support vector machines for imbalanced data sets Lect. Notes Artif. Intell. 4994 2008 38 47
-
(2008)
Lect. Notes Artif. Intell.
, vol.4994
, pp. 38-47
-
-
Wang, B.X.1
Japkowicz, N.2
-
32
-
-
17844387127
-
Neighbor-weighted k-nearest neighbor for unbalanced text corpus
-
S. Tan Neighbor-weighted k-nearest neighbor for unbalanced text corpus Expert Syst. Appl. 28 4 2005 667 671
-
(2005)
Expert Syst. Appl.
, vol.28
, Issue.4
, pp. 667-671
-
-
Tan, S.1
-
33
-
-
40649117682
-
Cost-sensitive learning in support vector machines
-
G. Fumera, F. Roli, Cost-sensitive learning in support vector machines, in: Proceedings of the Workshop Machine Learning, Methods and Applications, held in the Context of the 8th meeting of the Italian Association of Artificial Intelligence, 2002.
-
(2002)
Proceedings of the Workshop Machine Learning, Methods and Applications, Held in the Context of the 8th Meeting of the Italian Association of Artificial Intelligence
-
-
Fumera, G.1
Roli, F.2
-
38
-
-
84883447718
-
An insight into classification with imbalanced data: Empirical results and current trends on using intrinsic characteristics
-
V. Lopez, A. Fernandez, S. Garcia, V. Palade, and F. Herrera An insight into classification with imbalanced data: empirical results and current trends on using intrinsic characteristics Inf. Sci. 150 2013 113 141
-
(2013)
Inf. Sci.
, vol.150
, pp. 113-141
-
-
Lopez, V.1
Fernandez, A.2
Garcia, S.3
Palade, V.4
Herrera, F.5
-
40
-
-
78651375098
-
A survey of hierarchical classification across different application domains
-
C.N. Silla, and A.A. Freitas A survey of hierarchical classification across different application domains Data Min. Knowl. Discov. 22 1-2 2010 31 72
-
(2010)
Data Min. Knowl. Discov.
, vol.22
, Issue.12
, pp. 31-72
-
-
Silla, C.N.1
Freitas, A.A.2
-
42
-
-
26944498211
-
Learning classifiers using hierarchically structured class taxonomies
-
Springer
-
F. Wu, J. Zhang, and V. Honavar Learning classifiers using hierarchically structured class taxonomies Proceedings of the Symposium on Abstraction, Reformulation, and Approximation vol. 3607 2005 Springer 313 320
-
(2005)
Proceedings of the Symposium on Abstraction, Reformulation, and Approximation
, vol.3607
, pp. 313-320
-
-
Wu, F.1
Zhang, J.2
Honavar, V.3
-
45
-
-
84856461298
-
Selecting different protein representations and classification algorithms in hierarchical protein function prediction
-
C.N. Silla Jr., and A.A. Freitas Selecting different protein representations and classification algorithms in hierarchical protein function prediction Intell. Data Anal. J. 15 6 2011 979 999
-
(2011)
Intell. Data Anal. J.
, vol.15
, Issue.6
, pp. 979-999
-
-
Silla, Jr.C.N.1
Freitas, A.A.2
-
46
-
-
0036080105
-
Hierarchical fusion of multiple classifiers for hyperspectral data analysis
-
S. Kumar, J. Ghosh, and M.M. Crawford Hierarchical fusion of multiple classifiers for hyperspectral data analysis Pattern Anal. Appl. 5 2002 210 220
-
(2002)
Pattern Anal. Appl.
, vol.5
, pp. 210-220
-
-
Kumar, S.1
Ghosh, J.2
Crawford, M.M.3
-
47
-
-
33847644550
-
Hierarchically SVM classification based on support vector clustering method and its application to document categorization
-
P.Y. Hao, J.H. Chiang, and Y.K. Tu Hierarchically SVM classification based on support vector clustering method and its application to document categorization Expert Syst. Appl. 33 2007 627 635
-
(2007)
Expert Syst. Appl.
, vol.33
, pp. 627-635
-
-
Hao, P.Y.1
Chiang, J.H.2
Tu, Y.K.3
-
48
-
-
44349102595
-
Metaclasses and zoning mechanism applied to handwriting recognition
-
C.O.A. Freitas, L.S. Oliveira, S.B.K. Aires, and F. Bortolozzi Metaclasses and zoning mechanism applied to handwriting recognition J. Univers. Comput. Sci. 14 2 2008 211 223
-
(2008)
J. Univers. Comput. Sci.
, vol.14
, Issue.2
, pp. 211-223
-
-
Freitas, C.O.A.1
Oliveira, L.S.2
Aires, S.B.K.3
Bortolozzi, F.4
-
49
-
-
51749106543
-
Audio classification using binary hierarchical classifiers with feature selection for healthcare applications
-
Y. Peng, C. Lin, M. Sun, Audio classification using binary hierarchical classifiers with feature selection for healthcare applications, in: Proceedings of IEEE International Symposium on Circuits and Systems, 2008, pp. 3238-3241.
-
(2008)
Proceedings of IEEE International Symposium on Circuits and Systems
, pp. 3238-3241
-
-
Peng, Y.1
Lin, C.2
Sun, M.3
-
52
-
-
33847172327
-
Clustering by passing messages between data points
-
B.J. Frey, and D. Dueck Clustering by passing messages between data points Science 2007 972 997
-
(2007)
Science
, pp. 972-997
-
-
Frey, B.J.1
Dueck, D.2
-
53
-
-
55149107386
-
Multifeature object trajectory clustering for video analysis
-
N. Anjum, and A. Cavallaro Multifeature object trajectory clustering for video analysis IEEE Trans. Circuits Syst. Video Technol. 18 11 2008 1555 1564
-
(2008)
IEEE Trans. Circuits Syst. Video Technol.
, vol.18
, Issue.11
, pp. 1555-1564
-
-
Anjum, N.1
Cavallaro, A.2
-
55
-
-
84864039505
-
Laplacian score for feature selection
-
X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: Advances in Neural Information Processing Systems, vol. 18, 2005.
-
(2005)
Advances in Neural Information Processing Systems
, vol.18
-
-
He, X.1
Cai, D.2
Niyogi, P.3
-
56
-
-
77956216411
-
Unsupervised feature selection for multi-cluster data
-
D. Cai, C. Zhang, X. He, Unsupervised feature selection for multi-cluster data, in: Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2010, pp. 333-342.
-
(2010)
Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining
, pp. 333-342
-
-
Cai, D.1
Zhang, C.2
He, X.3
-
57
-
-
84879153481
-
An improved random forest algorithm for class-imbalanced data classification and its application in pad risk factors analysis
-
D. Yao, J. Yang, and Xiaojuan Zhan An improved random forest algorithm for class-imbalanced data classification and its application in pad risk factors analysis Open Electric. Electron. Eng. J. 7 2013 62 70
-
(2013)
Open Electric. Electron. Eng. J.
, vol.7
, pp. 62-70
-
-
Yao, D.1
Yang, J.2
Zhan, X.3
-
58
-
-
79951829331
-
KEEL data-mining software tool: Dataset repository, integration of algorithms and experimental analysis framework
-
J. Alcalá-Fdez, A. Fernández, J. Luengo, J. Derrac, S. García, L. Sánchez, and F. Herrera KEEL data-mining software tool: dataset repository, integration of algorithms and experimental analysis framework J. Mult.-Valued Logic Soft Comput. 17 2-3 2011 255 287
-
(2011)
J. Mult.-Valued Logic Soft Comput.
, vol.17
, Issue.23
, pp. 255-287
-
-
Alcalá-Fdez, J.1
Fernández, A.2
Luengo, J.3
Derrac, J.4
García, S.5
Sánchez, L.6
Herrera, F.7
-
61
-
-
0028026443
-
Neural expert system using fuzzy teaching input and its application to medical diagnosis
-
Y. Hayashi Neural expert system using fuzzy teaching input and its application to medical diagnosis Inf. Sci. Appl. 1 1994 47 58
-
(1994)
Inf. Sci. Appl.
, vol.1
, pp. 47-58
-
-
Hayashi, Y.1
-
62
-
-
0024111497
-
Using the ADAP learning algorithm to forecast the onset of diabetes mellitus
-
J.W. Smith, J.E. Everhart, W.C. Dickson, W.C. Knowler, R.S. Johannes, Using the ADAP learning algorithm to forecast the onset of diabetes mellitus, in: Proceedings of the Symposium on Computer Applications and Medical Care, 1988, pp. 261-265.
-
(1988)
Proceedings of the Symposium on Computer Applications and Medical Care
, pp. 261-265
-
-
Smith, J.W.1
Everhart, J.E.2
Dickson, W.C.3
Knowler, W.C.4
Johannes, R.S.5
-
64
-
-
3042597440
-
Learning multi-label scene classification
-
M.R. Boutell, J. Luo, X. Shen, and C.M. Brown Learning multi-label scene classification Pattern Recognit. 37 9 2004 1757 1771
-
(2004)
Pattern Recognit.
, vol.37
, Issue.9
, pp. 1757-1771
-
-
Boutell, M.R.1
Luo, J.2
Shen, X.3
Brown, C.M.4
-
65
-
-
0031998121
-
Machine learning for the detection of oil spills in satellite radar images
-
M. Kubat, R. Holte, and S. Matwin Machine learning for the detection of oil spills in satellite radar images Mach. Learn. 30 1998 195 215
-
(1998)
Mach. Learn.
, vol.30
, pp. 195-215
-
-
Kubat, M.1
Holte, R.2
Matwin, S.3
-
68
-
-
58149287952
-
An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons
-
S. García, and F. Herrera An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons J. Mach. Learn. Res. 9 2008 2677 2694
-
(2008)
J. Mach. Learn. Res.
, vol.9
, pp. 2677-2694
-
-
García, S.1
Herrera, F.2
-
69
-
-
77549084648
-
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
-
S. Garcia, A. Fernandez, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power Inf. Sci. 180 2010 2044 2064
-
(2010)
Inf. Sci.
, vol.180
, pp. 2044-2064
-
-
Garcia, S.1
Fernandez, A.2
Luengo, J.3
Herrera, F.4
-
70
-
-
29644438050
-
Statistical comparisons of classifiers over multiple datasets
-
J. Demsar Statistical comparisons of classifiers over multiple datasets J. Mach. Learn. Res. 7 2006 1 30
-
(2006)
J. Mach. Learn. Res.
, vol.7
, pp. 1-30
-
-
Demsar, J.1
-
71
-
-
0002294347
-
A simples equentially rejective multiple test procedure
-
S. Holm A simples equentially rejective multiple test procedure Scand. J. Stat. 6 1979 65 70
-
(1979)
Scand. J. Stat.
, vol.6
, pp. 65-70
-
-
Holm, S.1
|