14.1.4.2 Imbalanced Sample Sizes, Imbalanced Data

Chapter Contents (Back)
Imbalanced Data. Learning.

Barandela, R., Sánchez, J.S., García, V., Rangel, E.,
Strategies for learning in class imbalance problems,
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Sun, Y.M.[Yan-Min], Kamel, M.S.[Mohamed S.], Wong, A.K.C.[Andrew K.C.], Wang, Y.[Yang],
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Classification; Class imbalance problem; AdaBoost; Cost-sensitive learning BibRef

Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.,
SVMs Modeling for Highly Imbalanced Classification,
SMC-B(39), No. 1, February 2009, pp. 281-288.
IEEE DOI 0902
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Perez-Godoy, M.D.[Maria Dolores], Fernandez, A.[Alberto], Rivera, A.J.[Antonio Jesus], Jose del Jesus, M.[Maria],
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets,
PRL(31), No. 15, 1 November 2010, pp. 2375-2388.
Elsevier DOI 1003
Neural networks; Radial-basis function networks; Genetic algorithm; Imbalanced data sets; SMOTE pre-processing method BibRef

Fernandez-Navarro, F.[Francisco], Hervas-Martinez, C.[Cesar], Gutierrez, P.A.[Pedro Antonio],
A dynamic over-sampling procedure based on sensitivity for multi-class problems,
PR(44), No. 8, August 2011, pp. 1821-1833.
Elsevier DOI 1104
Classification; Multi-class; Sensitivity; Accuracy; Memetic algorithm; Imbalanced datasets; Over-sampling method; SMOTE BibRef

Soda, P.[Paolo],
A multi-objective optimisation approach for class imbalance learning,
PR(44), No. 8, August 2011, pp. 1801-1810.
Elsevier DOI 1104
Pattern recognition; Machine learning; Class imbalance learning; Multi-objective optimisation BibRef

Tahir, M.A.[Muhammad Atif], Kittler, J.V.[Josef V.], Bouridane, A.[Ahmed],
Multilabel classification using heterogeneous ensemble of multi-label classifiers,
PRL(33), No. 5, 1 April 2012, pp. 513-523.
Elsevier DOI 1202
Multilabel classification; Heterogeneous ensemble of multilabel classifiers; Static/dynamic weighting BibRef

Tahir, M.A.[Muhammad Atif], Kittler, J.V.[Josef V.], Yan, F.[Fei],
Inverse random under sampling for class imbalance problem and its application to multi-label classification,
PR(45), No. 10, October 2012, pp. 3738-3750.
Elsevier DOI 1206
Class imbalance problem; Multi-label classification; Inverse random under sampling BibRef

Thanathamathee, P.[Putthiporn], Lursinsap, C.[Chidchanok],
Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques,
PRL(34), No. 12, 1 September 2013, pp. 1339-1347.
Elsevier DOI 1306
Imbalanced data; Boundary data; Synthetic data generation; Bootstrap re-sampling; AdaBoost BibRef

Galar, M.[Mikel], Fernández, A.[Alberto], Barrenechea, E.[Edurne], Herrera, F.[Francisco],
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,
PR(46), No. 12, 2013, pp. 3460-3471.
Elsevier DOI 1308
Classification See also Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers. BibRef

Maldonado, S.[Sebastián], López, J.[Julio],
Imbalanced data classification using second-order cone programming support vector machines,
PR(47), No. 5, 2014, pp. 2070-2079.
Elsevier DOI 1402
Class-imbalanced data BibRef

Shao, Y.H.[Yuan-Hai], Chen, W.J.[Wei-Jie], Zhang, J.J.[Jing-Jing], Wang, Z.[Zhen], Deng, N.Y.[Nai-Yang],
An efficient weighted Lagrangian twin support vector machine for imbalanced data classification,
PR(47), No. 9, 2014, pp. 3158-3167.
Elsevier DOI 1406
Imbalanced data classification BibRef

Sun, Z.B.[Zhong-Bin], Song, Q.B.[Qin-Bao], Zhu, X.Y.[Xiao-Yan], Sun, H.[Heli], Xu, B.[Baowen], Zhou, Y.M.[Yu-Ming],
A novel ensemble method for classifying imbalanced data,
PR(48), No. 5, 2015, pp. 1623-1637.
Elsevier DOI 1502
Imbalanced data BibRef

d'Addabbo, A.[Annarita], Maglietta, R.[Rosalia],
Parallel selective sampling method for imbalanced and large data classification,
PRL(62), No. 1, 2015, pp. 61-67.
Elsevier DOI 1507
Imbalanced learning BibRef

Cheng, F.Y.[Fan-Yong], Zhang, J.[Jing], Wen, C.H.[Cui-Hong],
Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data,
PRL(80), No. 1, 2016, pp. 107-112.
Elsevier DOI 1609
Minimum margin BibRef

Sáez, J.A.[José A.], Krawczyk, B.[Bartosz], Wozniak, M.[Michal],
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets,
PR(57), No. 1, 2016, pp. 164-178.
Elsevier DOI 1605
Machine learning BibRef

Vluymans, S.[Sarah], Tarragó, D.S.[Dánel Sánchez], Saeys, Y.[Yvan], Cornelis, C.[Chris], Herrera, F.[Francisco],
Fuzzy rough classifiers for class imbalanced multi-instance data,
PR(53), No. 1, 2016, pp. 36-45.
Elsevier DOI 1602
Multi-instance learning BibRef

Ng, W.W.Y.[Wing W.Y.], Zeng, G.J.[Guang-Jun], Zhang, J.J.[Jiang-Jun], Yeung, D.S.[Daniel S.], Pedrycz, W.[Witold],
Dual autoencoders features for imbalance classification problem,
PR(60), No. 1, 2016, pp. 875-889.
Elsevier DOI 1609
Imbalanced Classification BibRef

Zhang, X.Z.[Xiu-Zhen], Li, Y.X.[Yu-Xuan], Kotagiri, R.[Ramamohanarao], Wu, L.F.[Li-Fang], Tari, Z.[Zahir], Cheriet, M.[Mohamed],
KRNN: k Rare-class Nearest Neighbour classification,
PR(62), No. 1, 2017, pp. 33-44.
Elsevier DOI 1705
Imbalanced classification BibRef

Zhu, C.M.[Chang-Ming], Wang, Z.[Zhe],
Entropy-based matrix learning machine for imbalanced data sets,
PRL(88), No. 1, 2017, pp. 72-80.
Elsevier DOI 1703
Entropy BibRef

Xu, Y.,
Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification,
Cyber(47), No. 6, June 2017, pp. 1540-1550.
IEEE DOI 1706
Computational efficiency, Cybernetics, Kernel, Linear programming, Minimization, Quadratic programming, Support vector machines, Homocentric sphere, imbalanced data classification, maximum margin, maximum margin of twin spheres support vector machine (MMTSSVM), twin, support, vector, machine, (TSVM) BibRef

Gónzalez, S.[Sergio], García, S.[Salvador], Lázaro, M.[Marcelino], Figueiras-Vidal, A.R.[Aníbal R.], Herrera, F.[Francisco],
Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets,
PR(70), No. 1, 2017, pp. 12-24.
Elsevier DOI 1706
Imbalanced classification BibRef

Devi, D.[Debashree], Biswas, S.K.[Saroj K.], Purkayastha, B.[Biswajit],
Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance,
PRL(93), No. 1, 2017, pp. 3-12.
Elsevier DOI 1706
Data, mining BibRef

Tang, B.[Bo], He, H.B.[Hai-Bo],
GIR-based ensemble sampling approaches for imbalanced learning,
PR(71), No. 1, 2017, pp. 306-319.
Elsevier DOI 1707
Imbalanced, learning BibRef

Zhu, T.F.[Tuan-Fei], Lin, Y.P.[Ya-Ping], Liu, Y.[Yonghe],
Synthetic minority oversampling technique for multiclass imbalance problems,
PR(72), No. 1, 2017, pp. 327-340.
Elsevier DOI 1708
Multiclass imbalance problems BibRef

Ortigosa-Hernández, J.[Jonathan], Inza, I.[Iñaki], Lozano, J.A.[Jose A.],
Measuring the class-imbalance extent of multi-class problems,
PRL(98), No. 1, 2017, pp. 32-38.
Elsevier DOI 1710
Class-imbalance, problem BibRef


Sze-To, A.[Antonio], Wong, A.K.C.[Andrew K. C.],
A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification,
ICIAR17(3-10).
Springer DOI 1706
BibRef

Cruz, R.[Ricardo], Fernandes, K.[Kelwin], Costa, J.F.P.[Joaquim F. Pinto], Ortiz, M.P.[María Pérez], Cardoso, J.S.[Jaime S.],
Ordinal Class Imbalance with Ranking,
IbPRIA17(3-12).
Springer DOI 1706
BibRef

Soleymani, R., Granger, E., Fumera, G.,
Loss factors for learning Boosting ensembles from imbalanced data,
ICPR16(204-209)
IEEE DOI 1705
Boosting, Electronic mail, Error analysis, Measurement, Pattern recognition, Standards, Training BibRef

Guan, H.[Hongjiao], Zhang, Y.T.[Ying-Tao], Xian, M.[Min], Cheng, H.D., Tang, X.L.[Xiang-Long],
WENN for individualized cleaning in imbalanced data,
ICPR16(456-461)
IEEE DOI 1705
Cleaning, Noise measurement, Robustness, Sensitivity, Shape, Training, WENN, data cleaning, imbalanced, data BibRef

Tax, D.M.J., Wang, F.,
Class-dependent, non-convex losses to optimize precision,
ICPR16(3314-3319)
IEEE DOI 1705
Labeling, Logistics, Neural networks, Optimization, Robustness, Standards, Training, Imbalanced classes, Multiple Instance Learning, Positive and Unlabeled data, Supervised learning, non-convex, optimization BibRef

Huang, C., Li, Y., Loy, C.C., Tang, X.,
Learning Deep Representation for Imbalanced Classification,
CVPR16(5375-5384)
IEEE DOI 1612
BibRef

Rong, T., Tian, X., Ng, W.W.Y.,
Location bagging-based undersampling for imbalanced classification problems,
ICWAPR16(72-77)
IEEE DOI 1611
Pattern recognition BibRef

Alejo, R.[Roberto], Monroy-de-Jesús, J.[Juan], Pacheco-Sánchez, J.H.[J. Horacio], Valdovinos, R.M.[Rosa María], Antonio-Velázquez, J.A.[Juan A.], Marcial-Romero, J.R.[J. Raymundo],
Analysing the Safe, Average and Border Samples on Two-Class Imbalance Problems in the Back-Propagation Domain,
CIARP15(699-707).
Springer DOI 1511
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Mera, C.[Carlos], Arrieta, J.[Jose], Orozco-Alzate, M.[Mauricio], Branch, J.[John],
A Bag Oversampling Approach for Class Imbalance in Multiple Instance Learning,
CIARP15(724-731).
Springer DOI 1511
BibRef

Mera, C.[Carlos], Orozco-Alzate, M.[Mauricio], Branch, J.[John],
Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning,
ICIAR14(I: 266-273).
Springer DOI 1410
BibRef

Fernández-Baldera, A.[Antonio], Buenaposada, J.M.[José M.], Baumela, L.[Luis],
Multi-class Boosting for Imbalanced Data,
IbPRIA15(57-64).
Springer DOI 1506
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García, V., Sánchez, J.S., Ochoa-Domínguez, H.J., Cleofas-Sánchez, L.,
Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts and Noise,
IbPRIA15(370-378).
Springer DOI 1506
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Famili, A.F.[A. Fazel],
Searching for Patterns in Imbalanced Data,
CIARP14(159-166).
Springer DOI 1411
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Kockentiedt, S.[Stephen], Tönnies, K.[Klaus], Gierke, E.[Erhardt],
Predicting the Influence of Additional Training Data on Classification Performance for Imbalanced Data,
GCPR14(377-387).
Springer DOI 1411
BibRef

Sandhan, T.[Tushar], Choi, J.Y.[Jin Young],
Handling Imbalanced Datasets by Partially Guided Hybrid Sampling for Pattern Recognition,
ICPR14(1449-1453)
IEEE DOI 1412
Databases BibRef

Giraldo-Forero, A.F.[Andrés Felipe], Jaramillo-Garzón, J.A.[Jorge Alberto], Ruiz-Muñoz, J.F.[José Francisco],
Managing Imbalanced Data Sets in Multi-label Problems: A Case Study with the SMOTE Algorithm,
CIARP13(I:334-342).
Springer DOI 1311
BibRef

Hernandez, J.[Julio], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
An Empirical Study of Oversampling and Undersampling for Instance Selection Methods on Imbalance Datasets,
CIARP13(I:262-269).
Springer DOI 1311
See also New Method for Skeleton Pruning, A. See also Prototype Selection for Graph Embedding Using Instance Selection. BibRef

Song, Y., Morency, L.P., Davis, R.,
Distribution-sensitive learning for imbalanced datasets,
FG13(1-6)
IEEE DOI 1309
data analysis. Datasets imbalanced across classes (faces, gestures) BibRef

Alejo, R., Toribio, P., Valdovinos, R.M., Pacheco-Sanchez, J.H.,
A Modified Back-Propagation Algorithm to Deal with Severe Two-Class Imbalance Problems on Neural Networks,
MCPR12(265-272).
Springer DOI 1208
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Alejo, R., Martínez Sotoca, J.[José], Casañ, G.A.,
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks,
CIARP08(479-486).
Springer DOI 0809
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Utasi, A.[Akos],
Weighted conditional mutual information based boosting for classification of imbalanced datasets,
ICPR12(2711-2714).
WWW Link. 1302
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d'Ambrosio, R.[Roberto], Iannello, G.[Giulio], Soda, P.[Paolo],
A One-per-Class reconstruction rule for class imbalance learning,
ICPR12(1310-1313).
WWW Link. 1302
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d'Ambrosio, R.[Roberto], Soda, P.[Paolo],
Polichotomies on Imbalanced Domains by One-per-Class Compensated Reconstruction Rule,
SSSPR12(301-309).
Springer DOI 1211
One of more classes underrepresented in training. BibRef

Millán-Giraldo, M.[Mónica], García, V.[Vicente], Sánchez, J.S.[J. Salvador],
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices,
SSSPR12(391-399).
Springer DOI 1211
BibRef

García, V.[Vicente], Sánchez, J.S.[Javier Salvador], Mollineda, R.A.[Ramón A.],
Classification of High Dimensional and Imbalanced Hyperspectral Imagery Data,
IbPRIA11(644-651).
Springer DOI 1106
BibRef
Earlier: A1, A3, A2:
Theoretical Analysis of a Performance Measure for Imbalanced Data,
ICPR10(617-620).
IEEE DOI 1008
BibRef
Earlier: A1, A3, A2:
Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions,
IbPRIA09(441-448).
Springer DOI 0906
BibRef
And: A1, A3, A2:
A New Performance Evaluation Method for Two-Class Imbalanced Problems,
SSPR08(917-925).
Springer DOI 0812
BibRef
Earlier: A1, A2, A3:
An Empirical Study of the Behavior of Classifiers on Imbalanced and Overlapped Data Sets,
CIARP07(397-406).
Springer DOI 0711
BibRef

García, V., Mollineda, R.A., Sánchez, J.S., Alejo, R., Martínez Sotoca, J.[José],
When Overlapping Unexpectedly Alters the Class Imbalance Effects,
IbPRIA07(II: 499-506).
Springer DOI 0706
BibRef

Ghanem, A.S.[Amal S.], Venkatesh, S.[Svetha], West, G.A.W.[Geoff A.W.],
Multi-class Pattern Classification in Imbalanced Data,
ICPR10(2881-2884).
IEEE DOI 1008
BibRef
Earlier:
Learning in imbalanced relational data,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Yin, D.[Dawei], An, C.[Chang], Baird, H.S.[Henry S.],
Imbalance and Concentration in k-NN Classification,
ICPR10(2170-2173).
IEEE DOI 1008
BibRef

Merler, M.[Michele], Yan, R.[Rong], Smith, J.R.[John R.],
Imbalanced RankBoost for efficiently ranking large-scale image/video collections,
CVPR09(2607-2614).
IEEE DOI 0906
BibRef

Ditzler, G.[Gregory], Polikar, R.[Robi], Chawla, N.V.[Nitesh V.],
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance,
ICPR10(2997-3000).
IEEE DOI 1008
BibRef

Nguyen, G.H.[Giang H.], Bouzerdoum, A.[Abdesselam], Phung, S.L.[Son L.],
A supervised learning approach for imbalanced data sets,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Molinara, M., Ricamato, M.T., Tortorella, F.,
Facing Imbalanced Classes through Aggregation of Classifiers,
CIAP07(43-48).
IEEE DOI 0709
BibRef

Cheng, H.T.[Hsien-Ting], Chen, C.S.[Chu-Song],
A Complementary Ordering Method for Class Imbalanced Problem,
ICPR06(III: 429-432).
IEEE DOI 0609
Asymmetric Bagging with Vector Complementary Ordering. Apply to biometrics. BibRef

Cantador, I.[Iván], Dorronsoro, J.R.[José R.],
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning,
IbPRIA05(II:43).
Springer DOI 0509
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Transfer Learning from Other Classes, Domain Adaptation .


Last update:Nov 18, 2017 at 20:56:18