KBA: kernel boundary alignment considering imbalanced data distribution
Abstract
An imbalanced training data set can pose serious problems for many real-world data mining tasks that employ SVMs to conduct supervised learning. In this paper, we propose a kernel-boundary-alignment algorithm, which considers THE training data imbalance as prior information to augment SVMs to improve class-prediction accuracy. Using a simple example, we first show that SVMs can suffer from high incidences of false negatives when the training instances of the target class are heavily outnumbered by the training instances of a nontarget class. The remedy we propose is to adjust the class boundary by modifying the kernel matrix, according to the imbalanced data distribution. Through theoretical analysis backed by empirical study, we show that our kernel-boundary-alignment algorithm works effectively on several data sets.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Imbalanced datasets are problematic for supervised machine learning models like Abstrackr, because the predictions are biased towards the majority non-relevant class at the expense of the minority-relevant class.
“Such imbalanced datasets are problematic for supervised machine learning models like Abstrackr, because the predictions are biased towards the majority non-relevant class at the expense of the minority-relevant class [22] and therefore produce many falsely weighted predictions, i.e. irrelevant citations.”
From Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers (2015) · cites this paper as [22]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,056 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217 · see citing works
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390 · see citing works
Introduction to statistical pattern recognition (2nd ed.)
Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,802 · see citing works
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,698 · see citing works
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.
Kubát, M., & Matwin, S. (1997) · Cited by 2,161 · see citing works
Kuhn, H. W., & Tucker, A. W. (1951) · University of California Press eBooks · doi:10.1525/9780520411586-036 · Cited by 1,995 · see citing works
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393 · see citing works
Support vector machine active learning for image retrieval
Tong, S., & Chang, E. Y. (2001) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/500141.500159 · Cited by 1,337 · see citing works
Self-citationImproving support vector machine classifiers by modifying kernel functions
Амари, Ш.-И., & Wu, S. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00032-5 · Cited by 941 · see citing works
Learning when training data are costly: the effect of class distribution on tree induction
Weiss, G. M., & Provost, F. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1199 · Cited by 921 · see citing works
Fawcett, T. E., & Provost, F. J. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009700419189 · Cited by 889 · see citing works
Cristianini, N., Kandola, J., Elisseeff, A., & Shawe-Taylor, J. S. (2006) · Studies in fuzziness and soft computing · doi:10.1007/3-540-33486-6_8 · Cited by 838 · see citing works
Controlling the Sensitivity of Support Vector Machines
Veropoulos, K., Campbell, I. C. G., & Cristianini, N. (1999) · Cited by 691 · see citing works
Support vector machine active learning for image retrieval
Tong, S., & Chang, E. Y. (2001) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/500156.500159 · Cited by 485 · see citing works
Self-citationCristianini, N., Shawe-Taylor, J. S., Elisseeff, A., & Kandola, J. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0052 · Cited by 475 · see citing works
Support Vector Machines for Classification in Nonstandard Situations
Lin, Y., Lee, Y., & Wahba, G. (2002) · Machine Learning · doi:10.1023/a:1012406528296 · Cited by 350 · see citing works
Adressing the curse of imbalanced training sets: one-sided selection
Kubát, M. (1997) · Medical Entomology and Zoology · Cited by 330 · see citing works
Geometry and Invariance in Kernel Based Methods
Burges, C. J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0010 · Cited by 240 · see citing works
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
Drummond, C., & Holte, R. C. (2000) · Cited by 200 · see citing works
Incorporating prior knowledge with weighted margin support vector machines
Xiaoyun, W., & Srihari, R. K. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014089 · Cited by 130 · see citing works
Improving Minority Class Prediction Using Case-Specific Feature Weights
Cardie, C., & Nowe, N. (1997) · Smith ScholarWorks (Smith College) · Cited by 109 · see citing works
Adaptive feature-space conformal transformation for imbalanced-data learning
Wu, G., & Chang, E. Y. (2003) · Cited by 99 · see citing works
Self-citationOptimizing Classifers for Imbalanced Training Sets
Karakoulas, G. J., & Shawe-Taylor, J. S. (1998) · neural information processing systems · Cited by 93 · see citing works
Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance
Wu, G., Wu, Y., Jiao, L., Wang, Y.-F., & Chang, E. Y. (2003) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/957013.957126 · Cited by 63 · see citing works
Self-citationIntriligator, M. D. (2011) · The New Palgrave Dictionary of Economics · doi:10.1057/9780230226203.3176 · Cited by 49 · see citing works
Optimizing classifiers for imbalanced training sets
Karakoulas, G. J., & Shawe-Taylor, J. S. (1999) · ePrints Soton (University of Southampton) · Cited by 32 · see citing works
A Solution for Imbalanced Training Sets Problem by CombNET-II and Its Application on Fog Forecasting
Nugroho, A. S., Kuroyanagi, S., & Iwata, A. (2002) · Institutional Repositories DataBase (IRDB) · Cited by 19 · see citing works
New ν-support vector machines and their sequential minimal optimization
Xiaoyun, W., & Srihari, R. K. (2003) · International Conference on Machine Learning · Cited by 18 · see citing works
Refining Kernels for Regression and Uneven Classification Problems
Kandola, J., & Shawe-Taylor, J. S. (2003) · Cited by 18 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359 · see citing works
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Shutao Li, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633969 · Cited by 4,945 · see citing works
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2161285 · Cited by 2,820 · see citing works
A comprehensive survey on support vector machine classification: Applications, challenges and trends
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.118 · Cited by 2,381 · see citing works
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129 · see citing works
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Khan, S. H., Hayat, M., Bennamoun, M., Sohel, F. A., & Togneri, R. B. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2732482 · Cited by 972 · see citing works
SVMs Modeling for Highly Imbalanced Classification
Tang, Y., Zhang, Y., Chawla, N. V., & Krasser, S. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2002909 · Cited by 969 · see citing works
Training and assessing classification rules with imbalanced data
Menardi, G., & Torelli, N. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0295-5 · Cited by 769 · see citing works
Borderline over-sampling for imbalanced data classification
Nguyen, H. M., Cooper, E. W., & Kamei, K. (2011) · International Journal of Knowledge Engineering and Soft Data Paradigms · doi:10.1504/ijkesdp.2011.039875 · Cited by 595 · see citing works
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
Galar, M., Fernández, A., Barrenechea, E., & Herrera, F. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.006 · Cited by 419 · see citing works
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
Batuwita, R., & Palade, V. (2010) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2010.2042721 · Cited by 412 · see citing works
AdaBoost with SVM-based component classifiers
Li, X., Wang, L., & Sung, E. (2007) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2007.07.001 · Cited by 393 · see citing works
Imbalanced Deep Learning by Minority Class Incremental Rectification
Dong, Q., Gong, S., & Zhu, X. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2832629 · Cited by 370 · see citing works
López, V., Fernández, A., Moreno-Torres, J. G., & Herrera, F. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.043 · Cited by 318 · see citing works
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310 · see citing works
A broad review on class imbalance learning techniques
Rezvani, S., & Wang, X. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110415 · Cited by 306 · see citing works
Class-imbalanced classifiers for high-dimensional data
Lin, W., & Chen, J. J. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs006 · Cited by 284 · see citing works
On strategies for imbalanced text classification using SVM: A comparative study
Sun, A., Lim, E., & Liu, Y. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.07.011 · Cited by 257 · see citing works
Systematic review on tool breakage monitoring techniques in machining operations
Li, X., Liu, X., Yue, C., Liang, S. Y., & Wang, L. (2022) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2022.103882 · Cited by 239 · see citing works
Novel Cost-Sensitive Approach to Improve the Multilayer Perceptron Performance on Imbalanced Data
de Castro, C. L., & Braga, A. P. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2246188 · Cited by 236 · see citing works
Class Imbalance Learning Methods for Support Vector Machines
Batuwita, R., & Palade, V. (2013) · doi:10.1002/9781118646106.ch5 · Cited by 235 · see citing works
Deep reinforcement learning for imbalanced classification
Lin, E., Chen, Q., & Qi, X. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01637-z · Cited by 223 · see citing works
Cost-sensitive support vector machines
Iranmehr, A., Masnadi-Shirazi, H., & Vasconcelos, N. M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.099 · Cited by 220 · see citing works
Handling data irregularities in classification: Foundations, trends, and future challenges
Das, S., Datta, S., & Chaudhuri, B. B. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.008 · Cited by 216 · see citing works
RAMOBoost: Ranked Minority Oversampling in Boosting
Chen, S., He, H., & Garcia, E. A. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2066988 · Cited by 208 · see citing works
Rathbone, J., Hoffmann, T., & Glasziou, P. (2015) · Systematic Reviews · doi:10.1186/s13643-015-0067-6 · Cited by 184 · see citing works
“…data-sets are problematic for supervised machine learning models like Abstrackr, because the predictions are biased towards the majority non-relevant class at the expense of the minority-relevant class [22] and therefore produce many falsely weighted predictions, i.e. irrelevant citations.”
BackgroundKim, M.-J., Kang, D.-K., & Kim, H. B. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.025 · Cited by 187 · see citing works
Quantifying counts and costs via classification
Forman, G. (2008) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0097-y · Cited by 174 · see citing works
Datta, S., & Das, S. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.06.005 · Cited by 166 · see citing works
RWO-Sampling: A random walk over-sampling approach to imbalanced data classification
Zhang, H., & Li, M. (2014) · Information Fusion · doi:10.1016/j.inffus.2013.12.003 · Cited by 166 · see citing works
A Survey of Predictive Modelling under Imbalanced DistributionsPreprint
Branco, P., Torgo, L., & Ribeiro, R. P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.01658 · Cited by 166 · see citing works
Adjusted F-measure and kernel scaling for imbalanced data learning
Maratea, A., Petrosino, A., & Manzo, M. (2013) · Information Sciences · doi:10.1016/j.ins.2013.04.016 · Cited by 156 · see citing works
Imbalance class problems in data mining: a review
Ali, H., Salleh, M. N. M., Saedudin, R. R., Hussain, K., & Mushtaq, M. F. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v14.i3.pp1552-1563 · Cited by 156 · see citing works
A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
Wang, Q., Luo, Z., Huang, J., Feng, Y., & Liu, Z. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/1827016 · Cited by 148 · see citing works
Singh, A. P., Ranjan, R. K., & Tiwari, A. (2021) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2021.1907795 · Cited by 147 · see citing works
z-SVM: An SVM for Improved Classification of Imbalanced Data
Imam, T., Ting, K. M., & Kamruzzaman, J. (2006) · Lecture notes in computer science · doi:10.1007/11941439_30 · Cited by 142 · see citing works
An Evaluation of the Robustness of MTS for Imbalanced Data
Su, C., & Hsiao, Y. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190623 · Cited by 138 · see citing works
RACOG and wRACOG: Two Probabilistic Oversampling Techniques
Das, B., Krishnan, N. C., & Cook, D. J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2324567 · Cited by 125 · see citing works
Susan, S., & Kumar, A. (2020) · Engineering Reports · doi:10.1002/eng2.12298 · Cited by 123 · see citing works
Coupling different methods for overcoming the class imbalance problem
Nanni, L., Fantozzi, C., & Lazzarini, N. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.068 · Cited by 119 · see citing works
Fernández, A., del Jesús, M. J., & Herrera, F. (2010) · Information Sciences · doi:10.1016/j.ins.2009.12.014 · Cited by 113 · see citing works
SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling
Guan, H., Zhang, Y., Xian, M., Cheng, H.-D., & Tang, X. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01852-8 · Cited by 113 · see citing works
A resampling ensemble algorithm for classification of imbalance problems
Qian, Y., Liang, Y., Li, M., Feng, G., & Shi, X. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.06.021 · Cited by 111 · see citing works
KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning
Tang, B., & He, H. (2015) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2015.7256954 · Cited by 109 · see citing works
Yu, M.-C., Yu, T., Wang, S.-C., Lin, C., & Chang, E. Y. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2733004.2733015 · Cited by 106 · see citing works
Self-citationYu, H., & Ni, J. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2306838 · Cited by 100 · see citing works
PSO-based method for SVM classification on skewed data sets
Cervantes, J., García‐Lamont, F., Rodríguez, L., López‐Chau, A., Castilla, J. R., & Espinosa, A. T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.041 · Cited by 93 · see citing works
Model-Based Synthetic Sampling for Imbalanced Data
Liu, C., & Hsieh, P.-Y. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2905559 · Cited by 93 · see citing works
Ahmad, H., Kasasbeh, B., Aldabaybah, B., & Rawashdeh, E. F. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00987-w · Cited by 88 · see citing works
An empirical evaluation of sampling methods for the classification of imbalanced data
Kim, M., & Hwang, K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271260 · Cited by 87 · see citing works
A classification based approach to speech segregation
Han, K., & Wang, D. (2012) · The Journal of the Acoustical Society of America · doi:10.1121/1.4754541 · Cited by 86 · see citing works
Kernel-based SMOTE for SVM classification of imbalanced datasets
Mathew, J., Luo, M., Pang, C. K., & Chan, H. L. (2015) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2015.7392251 · Cited by 86 · see citing works
Evolutionary under-sampling based bagging ensemble method for imbalanced data classification
Sun, B., Chen, H., Wang, J., & Xie, H. (2017) · Frontiers of Computer Science · doi:10.1007/s11704-016-5306-z · Cited by 85 · see citing works
Moravvej, S. V., Alizadehsani, R., Khanam, S., Sobhaninia, Z., Shoeibi, A., Khozeimeh, F., Sani, Z. A., Tan, R., Khosravi, A., Nahavandi, S., et al. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/8733632 · Cited by 81 · see citing works
Transfer Boosting With Synthetic Instances for Class Imbalanced Object Recognition
Zhang, X., Zhuang, Y., Wang, W., & Pedrycz, W. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2636370 · Cited by 79 · see citing works
Abidine, M. B., Fergani, L., Fergani, B., & Oussalah, M. (2016) · Pattern Analysis and Applications · doi:10.1007/s10044-016-0570-y · Cited by 79 · see citing works
GIR-based ensemble sampling approaches for imbalanced learning
Tang, B., & He, H. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.06.019 · Cited by 78 · see citing works
Kabir, M., Arif, M., Ahmad, S., Ali, Z., Swati, Z. N. K., & Yu, D. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2018.09.007 · Cited by 78 · see citing works
Learning from Imbalanced Data: Evaluation Matters
Raeder, T., Forman, G., & Chawla, N. V. (2011) · Intelligent systems reference library · doi:10.1007/978-3-642-23166-7_12 · Cited by 77 · see citing works
Margin calibration in SVM class-imbalanced learning
Yang, C., Yang, J.-S., & Wang, J. (2009) · Neurocomputing · doi:10.1016/j.neucom.2009.08.006 · Cited by 74 · see citing works
Oversampling the Minority Class in the Feature Space
Pérez‐Ortiz, M., Gutiérrez, P. A., Peter Tiňo, B., & Hervás‐Martínez, C. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2461436 · Cited by 74 · see citing works
Knowledge acquisition through information granulation for imbalanced data
Su, C., Chen, L., & Yih, Y. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.082 · Cited by 73 · see citing works
Risk minimization, probability elicitation, and cost-sensitive SVMs
Masnadi-Shirazi, H., & Vasconcelos, N. M. (2010) · Cited by 70 · see citing works
Imbalanced data classification based on scaling kernel-based support vector machine
Zhang, Y., Fu, P., Liu, W., & Chen, G. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1584-2 · Cited by 70 · see citing works
Xu, L., Chow, M., Timmis, J., & Taylor, L. S. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.889040 · Cited by 70 · see citing works
A novel method for mining highly imbalanced high-throughput screening data in PubChem
Li, Q., Wang, Y., & Bryant, S. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp589 · Cited by 66 · see citing works
Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Tan, S. C., Watada, J., Ibrahim, Z., & Khalid, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2329097 · Cited by 65 · see citing works
Quantifying trends accurately despite classifier error and class imbalance
Forman, G. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150423 · Cited by 64 · see citing works
PDFOS: PDF estimation based over-sampling for imbalanced two-class problems
Gao, M., Hong, X., Chen, S., Harris, C. J., & Khalaf, E. F. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.02.006 · Cited by 64 · see citing works
Li, J., Fong, S. J., Mohammed, S., & Fiaidhi, J. (2015) · The Journal of Supercomputing · doi:10.1007/s11227-015-1541-6 · Cited by 63 · see citing works
A reference model for customer-centric data mining with support vector machines
Lessmann, S., & Voß, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2008.12.017 · Cited by 86 · see citing works
Hu, J. S., He, X., Yu, D., Yang, X., Yang, J.-Y., & Shen, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107676 · Cited by 58 · see citing works
Intelligent operation of heavy haul train with data imbalance: A machine learning method
Wang, X., Li, S., Tang, T., Wang, X., & Xun, J. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.08.015 · Cited by 57 · see citing works
Machine Learning for Speaker Recognition
Mak, M., & Chien, J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108552332 · Cited by 55 · see citing works
Ketu, S., & Mishra, P. K. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00435-5 · Cited by 55 · see citing works
Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units
Haddad, B. M., Yang, S., Karam, L. J., Ye, J., Patel, N. S., & Braun, M. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2594288 · Cited by 55 · see citing works
An asymmetric classifier based on partial least squares
Qu, H., Li, G., & Xu, W. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.05.002 · Cited by 54 · see citing works
Learning concepts from large scale imbalanced data sets using support cluster machines
Yuan, J., Li, J., & Zhang, B. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180729 · Cited by 53 · see citing works
Su, C., Wang, P., Chen, Y.-C., & Chen, L. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9706-1 · Cited by 53 · see citing works
Generative adversarial networks and their application to 3D face generation: A survey
Toshpulatov, M., Lee, W., & Lee, S. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104119 · Cited by 56 · see citing works
Class imbalance learning via a fuzzy total margin based support vector machine
Dai, H.-L. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.02.025 · Cited by 52 · see citing works
Medical imbalanced data classification
Belarouci, S., & Chikh, M. A. (2017) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj020316 · Cited by 45 · see citing works
Predicting minority class for suspended particulate matters level by extreme learning machine
Vong, C., Ip, W., Wong, P.-K., & Chiu, C.-C. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.11.056 · Cited by 51 · see citing works
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Abril, L. G., Núñez, H., Ángulo, C., & Morente, F. V. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2013.12.013 · Cited by 51 · see citing works
Class imbalance and the curse of minority hubs
Tomašev, N., & Mladenić, D. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.08.031 · Cited by 51 · see citing works
A comparative study of the class imbalance problem in Twitter spam detection
Li, C., & Liu, S. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4281 · Cited by 52 · see citing works
Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines
Rezvani, S., & Wang, X. (2021) · Information Sciences · doi:10.1016/j.ins.2021.07.010 · Cited by 51 · see citing works
Imputation-based Ensemble Techniques for Class Imbalance Learning
Razavi‐Far, R., Farajzadeh-Zanjani, M., Wang, B., Saif, M., & Chakrabarti, S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2951556 · Cited by 49 · see citing works
Utterance partitioning with acoustic vector resampling for GMM–SVM speaker verification
Mak, M., & Rao, W. (2010) · Speech Communication · doi:10.1016/j.specom.2010.06.011 · Cited by 48 · see citing works
Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation
Tang, J., Li, J., Xu, W., Tian, Y., Ju, X., & Zhang, J. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.06.016 · Cited by 47 · see citing works
Multiobjective Support Vector Machines: Handling Class Imbalance With Pareto Optimality
Datta, S., & Das, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869298 · Cited by 46 · see citing works
Xu, G., Zhou, H., & Chen, J. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.05.007 · Cited by 46 · see citing works
Learning Sequential Behavior Representations for Fraud Detection
Guo, J., Liu, G., Zuo, Y., & Wu, J. (2018) · Proceedings · doi:10.1109/icdm.2018.00028 · Cited by 46 · see citing works
Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction
Tang, Y., & Zhang, Y. (2006) · IEEE International Conference on Granular Computing (GrC) · doi:10.1109/grc.2006.1635839 · Cited by 46 · see citing works
Long-tailed visual recognition with deep models: A methodological survey and evaluation
Fu, Y., Xiang, L., Zahid, Y., Ding, G.-G., Mei, T., Shen, Q., & Han, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.031 · Cited by 45 · see citing works
Shen, M., Su, B., Esposito, E. X., Hopfinger, A. J., & Tseng, Y. J. (2011) · Chemical Research in Toxicology · doi:10.1021/tx200099j · Cited by 44 · see citing works
Talking human face generation: A survey
Toshpulatov, M. A., Lee, W., & Lee, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119678 · Cited by 42 · see citing works
Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias
Núñez, H., Abril, L. G., & Ángulo, C. (2017) · Journal of Classification · doi:10.1007/s00357-017-9242-x · Cited by 41 · see citing works
Bayes Vector Quantizer for Class-Imbalance Problem
Diamantini, C., & Potena, D. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.187 · Cited by 41 · see citing works
Facing Imbalanced Classes through Aggregation of Classifiers
Molinara, M., Ricamato, M. T., & Tortorella, F. (2007) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.2007.4362755 · Cited by 41 · see citing works
- Authors
- Wu, G., & Chang, E. Y.
- Affiliations
- University of California, Santa Barbara (US)
- ORCID
- Wu, G.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2005-06
- Volume
- 17
- Issue
- 6
- Pages
- 786-795
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2099435377
- Cited by
- 380
- References
- 39
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 8 · 2023: 16 · 2022: 17 · 2021: 17 · 2020: 30 · 2019: 24 · 2018: 24 · 2017: 15 · 2016: 20 · 2015: 26 · 2014: 30 · 2013: 28 · 2012: 16
- FWCI
- 15.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- class imbalance, support vector machine, false-negative results, minority class
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
