The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
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
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Using AUC and accuracy in evaluating learning algorithms
Huang, J., & Ling, C. X. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.50 · Cited by 2,148
Dataset Shift in Machine Learning
(2008) · doi:10.7551/mitpress/9780262170055.001.0001 · Cited by 1,553
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Research works printed but not linked: 127
- Alcalá-Fdez, 2011, KEEL data–mining software tool: data set repository, integration of algorithms and experimental analysis framework, Journal of Multi-Valued Logic and Soft Computing, 17, 255
- Alcalá;-Fdez, 2009, KEEL: a software tool to assess evolutionary algorithms for data mining problems, Soft Computing, 13, 307, 10.1007/s00500-008-0323-y · DOI · Paper page
- Anand, 2010, An approach for classification of highly imbalanced data using weighting and undersampling, Amino Acids, 39, 1385, 10.1007/s00726-010-0595-2 · DOI · Paper page
- Barandela, 2003, Strategies for learning in class imbalance problems, Pattern Recognition, 36, 849, 10.1016/S0031-3203(02)00257-1 · DOI · Paper page
- Barandela, 2003, New applications of ensembles of classifiers, Pattern Analysis Applications, 6, 245, 10.1007/s10044-003-0192-z · DOI · Paper page
- Batista, 2004, A study of the behaviour of several methods for balancing machine learning training data, SIGKDD Explorations, 6, 20, 10.1145/1007730.1007735 · DOI · Paper page
- R. Batuwita, V. Palade, AGm: a new performance measure for class imbalance learning. application to bioinformatics problems, in: Proceedings of the 8th International Conference on Machine Learning and Applications (ICMLA 2009), 2009, pp. 545–550. · DOI
- Batuwita, 2009, microPred: effective classification of pre-miRNAs for human miRNA gene prediction, Bioinformatics, 25, 989, 10.1093/bioinformatics/btp107 · DOI · Paper page
- R. Batuwita, V. Palade, Efficient resampling methods for training support vector machines with imbalanced datasets, in: Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), 2010. · DOI
- Batuwita, 2010, FSVM-CIL: fuzzy support vector machines for class imbalance learning, IEEE Transactions on Fuzzy Systems, 18, 558, 10.1109/TFUZZ.2010.2042721 · DOI · Paper page
- Batuwita, 2012, Adjusted geometric-mean: a novel performance measure for imbalanced bioinformatics datasets learning, Journal of Bioinformatics and Computational Biology, 10, 10.1142/S0219720012500035 · DOI
- Bickel, 2009, Discriminative learning under covariate shift, Journal of Machine Learning Research, 10, 2137
- Błaszczyński, 2010, Integrating selective pre-processing of imbalanced data with ivotes ensemble, vol. 6086, 148
- J.P. Bradford, C. Kunz, R. Kohavi, C. Brunk, C.E. Brodley, Pruning decision trees with misclassification costs, in: Proceedings of the 10th European Conference on Machine Learning (ECML’98), 1998, pp. 131–136. · DOI · Paper page
- C. Bunkhumpornpat, K. Sinapiromsaran, C. Lursinsap, Safe–level–SMOTE: Safe–level–synthetic minority over–sampling TEchnique for handling the class imbalanced problem. In: Proceedings of the 13th Pacific–Asia Conference on Advances in Knowledge Discovery and Data Mining PAKDD’09, 2009, pp. 475–482. · DOI · Paper page
- Bunkhumpornpat, 2012, DBSMOTE: density-based synthetic minority over-sampling technique, Applied Intelligence, 36, 664, 10.1007/s10489-011-0287-y · DOI · Paper page
- Carvalho, 2004, A hybrid decision tree/genetic algorithm method for data mining, Information Sciences, 163, 13, 10.1016/j.ins.2003.03.013 · DOI
- P.K. Chan, S.J. Stolfo, Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection, in: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD’98). 1998. pp. 164–168.
- E.Y. Chang, B. Li, G. Wu, K. Goh, Statistical learning for effective visual information retrieval, in: Proceedings of the 2003 International Conference on Image Processing (ICIP’03), vol. 3, 2003, pp. 609–612. · DOI
- Chawla, 2008, Automatically countering imbalance and its empirical relationship to cost, Data Mining and Knowledge Discovery, 17, 225, 10.1007/s10618-008-0087-0 · DOI · Paper page
- Chawla, 2004, Editorial: special issue on learning from imbalanced data sets, SIGKDD Explorations, 6, 1, 10.1145/1007730.1007733 · DOI · Paper page
- N.V. Chawla, A. Lazarevic, L.O. Hall, K.W. Bowyer, SMOTEBoost: Improving prediction of the minority class in boosting, in: Proceedings of 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’03), 2003, pp. 107–119. · DOI · Paper page
- Chen, 2011, Graph-based feature selection for object-oriented classification in VHR airborne imagery, IEEE Transactions on Geoscience and Remote Sensing, 49, 353, 10.1109/TGRS.2010.2054832 · doi:10.1109/tgrs.2010.2054832
- D.A. Cieslak, N.V. Chawla, Analyzing pets on imbalanced datasets when training and testing class distributions differ, in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD08). Osaka, Japan, 2008, pp. 519–526. · doi:10.1007/978-3-540-68125-0_46
- Cieslak, 2009, A framework for monitoring classifiers’ performance: when and why failure occurs?, Knowledge and Information Systems, 18, 83, 10.1007/s10115-008-0139-1 · DOI
- Cohen, 2006, Learning from imbalanced data in surveillance of nosocomial infection, Artificial Intelligence in Medicine, 37, 7, 10.1016/j.artmed.2005.03.002 · DOI · Paper page
- M. Denil, T. Trappenberg, Overlap versus imbalance, in: Proceedings of the 23rd Canadian Conference on Advances in Artificial Intelligence (CCAI’10), Lecture Notes on Artificial Intelligence, vol. 6085, 2010, pp. 220–231. · DOI
- P. Domingos, Metacost: a general method for making classifiers cost–sensitive, in: Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD’99), 1999, pp. 155–164. · DOI · Paper page
- Drown, 2009, Evolutionary sampling and software quality modeling of high-assurance systems, IEEE Transactions on Systems, Man, and Cybernetics, Part A, 39, 1097, 10.1109/TSMCA.2009.2020804 · DOI
- Drummond, 2006, Cost curves: an improved method for visualizing classifier performance, Machine Learning, 65, 95, 10.1007/s10994-006-8199-5 · DOI · Paper page
- Ducange, 2010, Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets, Soft Computing, 14, 713, 10.1007/s00500-009-0460-y · DOI
- C. Elkan, The foundations of cost–sensitive learning, in: Proceedings of the 17th IEEE International Joint Conference on Artificial Intelligence (IJCAI’01), 2001, pp. 973–978.
- Estabrooks, 2004, A multiple resampling method for learning from imbalanced data sets, Computational Intelligence, 20, 18, 10.1111/j.0824-7935.2004.t01-1-00228.x · DOI · Paper page
- Fernández, 2010, On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets, Information Sciences, 180, 1268, 10.1016/j.ins.2009.12.014 · DOI
- Fernández, 2008, A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets, Fuzzy Sets and Systems, 159, 2378, 10.1016/j.fss.2007.12.023 · DOI · Paper page
- Fernandez, 2010, Genetics-based machine learning for rule induction: state of the art, taxonomy and comparative study, IEEE Transactions on Evolutionary Computation, 14, 913, 10.1109/TEVC.2009.2039140 · DOI · Paper page
- Fernández, 2013, Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches, Knowledge-Based Systems, 42, 97, 10.1016/j.knosys.2013.01.018 · DOI · Paper page
- J.H. Friedman, R. Kohavi, Y. Yun, Lazy decision trees, in: Proceedings of the AAAI/IAAI, vol. 1, 1996, pp. 717–724.
- Galar, 2012, A review on ensembles for class imbalance problem: bagging, boosting and hybrid based approaches, IEEE Transactions on Systems, Man, and Cybernetics – part C: Applications and Reviews, 42, 463, 10.1109/TSMCC.2011.2161285 · DOI · Paper page
- García, 2012, Evolutionary-based selection of generalized instances for imbalanced classification, Knowledge Based Systems, 25, 3, 10.1016/j.knosys.2011.01.012 · DOI
- García, 2009, Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems, Applied Soft Computing, 9, 1304, 10.1016/j.asoc.2009.04.004 · DOI
- García, 2009, Evolutionary under-sampling for classification with imbalanced data sets: proposals and taxonomy, Evolutionary Computation, 17, 275, 10.1162/evco.2009.17.3.275 · DOI · Paper page
- V. García, R.A. Mollineda, J.S. Sánchez, A new performance evaluation method for two-class imbalanced problems, in: Proceedings of the Structural and Syntactic Pattern Recognition (SSPR’08) and Statistical Techniques in Pattern Recognition (SPR’08), Lecture Notes on Computer Science, vol. 5342, 2008, pp. 917–925. · DOI
- García, 2008, On the k-NN performance in a challenging scenario of imbalance and overlapping, Pattern Analysis Applications, 11, 269, 10.1007/s10044-007-0087-5 · DOI · Paper page
- V. García, R.A. Mollineda, J.S. Sánchez, Theoretical analysis of a performance measure for imbalanced data, in: 20th International Conference on Pattern Recognition (ICPR’10), 2010, pp. 617–620. · DOI
- García, 2012, Classifier performance assessment in two-class imbalanced problems, Internal Communication.
- García, 2012, On the effectiveness of preprocessing methods when dealing with different levels of class imbalance, Knowledge Based Systems, 25, 13, 10.1016/j.knosys.2011.06.013 · DOI · Paper page
- García-Pedrajas, 2012, Class imbalance methods for translation initiation site recognition in DNA sequences, Knowledge Based Systems, 25, 22, 10.1016/j.knosys.2011.05.002 · doi:10.1016/j.knosys.2011.05.002
- Guo, 2004, Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach, SIGKDD Explorations Newsletter, 6, 30, 10.1145/1007730.1007736 · DOI · Paper page
- H. Han, W.Y. Wang, B.H. Mao, Borderline–SMOTE: a new over–sampling method in imbalanced data sets learning, in: Proceedings of the 2005 International Conference on Intelligent Computing (ICIC’05), Lecture Notes in Computer Science, vol. 3644, 2005, pp. 878–887. · DOI · Paper page
- Hart, 1968, The condensed nearest neighbor rule, IEEE Transactions on Information Theory, 14, 515, 10.1109/TIT.1968.1054155 · DOI · Paper page
- H. He, Y. Bai, E.A. Garcia, S. Li, ADASYN: adaptive synthetic sampling approach for imbalanced learning, in: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN’08), 2008, pp. 1322–1328. · DOI · Paper page
- He, 2009, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21, 1263, 10.1109/TKDE.2008.239 · DOI · Paper page
- Hido, 2009, Roughly balanced bagging for imbalanced data, Statistical Analysis and Data Mining, 2, 412, 10.1002/sam.10061 · DOI · Paper page
- R.C. Holte, L. Acker, B.W. Porter, Concept learning and the problem of small disjuncts, in: Proceedings of the International Joint Conferences on Artificial Intelligence, IJCAI’89, 1989, pp. 813–818.
- S. Hu, Y. Liang, L. Ma, Y. He, MSMOTE: improving classification performance when training data is imbalanced, in: Proceedings of the 2nd International Workshop on Computer Science and Engineering (WCSE’09), vol. 2, 2009, pp. 13–17. · DOI · Paper page
- Japkowicz, 2001, Concept-learning in the presence of between-class and within-class imbalances, vol. 2056, 67
- Jo, 2004, Class imbalances versus small disjuncts, ACM SIGKDD Explorations Newsletter, 6, 40, 10.1145/1007730.1007737 · DOI · Paper page
- Khoshgoftaar, 2011, Comparing boosting and bagging techniques with noisy and imbalanced data, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 41, 552, 10.1109/TSMCA.2010.2084081 · DOI · Paper page
- M. Kubat, S. Matwin, Addressing the curse of imbalanced training sets: one-sided selection, in: Proceedings of the 14th International Conference on Machine Learning (ICML’97), 1997, pp. 179–186.
- Kuncheva, 2013, A weighted voting framework for classifiers ensembles, Knowledge and Information Systems
- Kwak, 2008, Feature extraction for classification problems and its application to face recognition, Pattern Recognition, 41, 1718, 10.1016/j.patcog.2007.10.012 · DOI · Paper page
- J. Laurikkala, Improving identification of difficult small classes by balancing class distribution, in: Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine (AIME’01), 2001, pp. 63–66. · DOI · Paper page
- Li, 2007, Classifying imbalanced data using a bagging ensemble variation (BEV), vol. 45, 203
- Lin, 2013, Dynamic sampling approach to training neural networks for multiclass imbalance classification, IEEE Transactions on Neural Networks and Learning Systems, 24, 647, 10.1109/TNNLS.2012.2228231 · DOI · Paper page
- Lin, 2013, Class-imbalanced classifiers for high-dimensional data, Briefings in Bioinformatics, 14, 13, 10.1093/bib/bbs006 · DOI · Paper page
- C.X. Ling, C. Li, Data mining for direct marketing: Problems and solutions, in: Proceedings of the 4th International Conference on Knownledge Discovery and Data Mining (KDD’98), 1998, pp. 73–79.
- Ling, 2004, Decision trees with minimal costs, vol. 69, 69
- Liu, 2009, Exploratory undersampling for class-imbalance learning, IEEE Transactions on System, Man and Cybernetics B, 39, 539, 10.1109/TSMCB.2008.2007853 · DOI · Paper page
- Lo, 2008, Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method, SIGKDD Explorations, 10, 43, 10.1145/1540276.1540290 · DOI
- López, 2013, A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets, Knowledge-Based Systems, 38, 85, 10.1016/j.knosys.2012.08.025 · DOI
- López, 2012, Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics, Expert Systems with Applications, 39, 6585, 10.1016/j.eswa.2011.12.043 · DOI · Paper page
- Luengo, 2011, Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling, Soft Computing, 15, 1909, 10.1007/s00500-010-0625-8 · DOI · Paper page
- R. Martín-Félez, R.A., Mollineda, On the suitability of combining feature selection and resampling to manage data complexity, in: Proceedings of the Conferencia de la Asociacin Espaola de Inteligencia Artificial (CAEPIA’09), Lecture Notes on Artificial Intelligence, vol. 5988, 2010, pp. 141–150. · doi:10.1007/978-3-642-14264-2_15
- Mazurowski, 2008, Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance, Neural Networks, 21
- Mena, 2009, Symbolic one-class learning from imbalanced datasets: application in medical diagnosis, International Journal on Artificial Intelligence Tools, 18, 273, 10.1142/S0218213009000135 · DOI
- J.G. Moreno-Torres, F. Herrera, A preliminary study on overlapping and data fracture in imbalanced domains by means of genetic programming-based feature extraction, in: Proceedings of the 10th International Conference on Intelligent Systems Design and Applications (ISDA’10), 2010, pp. 501–506. · DOI
- Moreno-Torres, 2013, Repairing fractures between data using genetic programming-based feature extraction: a case study in cancer diagnosis, Information Sciences, 222, 805, 10.1016/j.ins.2010.09.018 · DOI
- Moreno-Torres, 2012, A unifying view on dataset shift in classification, Pattern Recognition, 45, 521, 10.1016/j.patcog.2011.06.019 · DOI · Paper page
- K. Napierala, J. Stefanowski, S. Wilk, Learning from imbalanced data in presence of noisy and borderline examples, in: Proceedings of the 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC’10), Lecture Notes on Artificial Intelligence, vol. 6086, 2010, pp. 158–167. · DOI · Paper page
- Orriols-Puig, 2009, Evolutionary rule-based systems for imbalanced datasets, Soft Computing, 13, 213, 10.1007/s00500-008-0319-7 · DOI · Paper page
- Orriols-Puig, 2009, Facetwise analysis of XCS for problems with class imbalances, IEEE Transactions on Evolutionary Computation, 13, 260, 10.1109/TEVC.2008.925144 · DOI
- Polikar, 2006, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 6, 21, 10.1109/MCAS.2006.1688199 · DOI · Paper page
- R.C. Prati, G.E.A.P.A., Batista, Class imbalances versus class overlapping: an analysis of a learning system behavior, in: Proceedings of the 2004 Mexican International Conference on Artificial Intelligence (MICAI’04), 2004, pp. 312–321. · DOI · Paper page
- Prati, 2011, A survey on graphical methods for classification predictive performance evaluation, IEEE Transactions on Knowledge and Data Engineering, 23, 1601, 10.1109/TKDE.2011.59 · DOI
- Raudys, 1991, Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 252, 10.1109/34.75512 · DOI · Paper page
- Riddle, 1994, Representation design and brute-force induction in a boeing manufacturing domain, Applied Artificial Intelligence, 8, 125, 10.1080/08839519408945435 · DOI
- R.E. Schapire, A brief introduction to boosting, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’99), 1999, pp. 1401–1406.
- Seiffert, 2013, An empirical study of the classification performance of learners on imbalanced and noisy software quality data, Information Sciences
- Seiffert, 2010, RUSBoost: a hybrid approach to alleviating class imbalance, IEEE Transactions on System, Man and Cybernetics A, 40, 185, 10.1109/TSMCA.2009.2029559 · DOI · Paper page
- Shaffer, 1986, Modified sequentially rejective multiple test procedures, Journal of the American Statistical Association, 81, 826, 10.1080/01621459.1986.10478341 · DOI · Paper page
- Shimodaira, 2000, Improving predictive inference under covariate shift by weighting the log-likelihood function, Journal of Statistical Planning and Inference, 90, 227, 10.1016/S0378-3758(00)00115-4 · DOI · Paper page
- J. Stefanowski, S. Wilk, Improving rule based classifiers induced by MODLEM by selective pre-processing of imbalanced data, in: Proceedings of the RSKD Workshop at ECML/PKDD’07, 2007, pp. 54–65.
- J. Stefanowski, S. Wilk, Selective pre-processing of imbalanced data for improving classification performance, in: Proceedings of the 10th International Conference on Data Warehousing and Knowledge, Discovery (DaWaK08), 2008, pp. 283–292. · DOI · Paper page
- Sun, 2007, Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition, 40, 3358, 10.1016/j.patcog.2007.04.009 · DOI · Paper page
- Sun, 2009, Classification of imbalanced data: a review, International Journal of Pattern Recognition and Artificial Intelligence, 23, 687, 10.1142/S0218001409007326 · DOI · Paper page
- Tang, 2009, SVMs modeling for highly imbalanced classification, IEEE Transactions on Systems, Man and Cybernetics, Part B, 9, 281, 10.1109/TSMCB.2008.2002909 · DOI · Paper page
- Tao, 2006, Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1088, 10.1109/TPAMI.2006.134 · DOI · Paper page
- K.M. Ting, The problem of small disjuncts: its remedy in decision trees, in: Proceedings of the 10th Canadian Conference on Artificial Intelligence (CCAI’94), 1994, pp. 91–97.
- K.M. Ting, A comparative study of cost-sensitive boosting algorithms, in: Proceedings of the 17th International Conference on Machine Learning (ICML’00), Stanford, CA, USA, 2000, pp. 983–990.
- Ting, 2002, An instance-weighting method to induce cost-sensitive trees, IEEE Transactions on Knowledge and Data Engineering, 14, 659, 10.1109/TKDE.2002.1000348 · DOI · Paper page
- Tomek, 1976, Two modifications of CNN, IEEE Transactions on Systems Man and Communications, 6, 769
- Tsai, 2009, Forecasting of ozone episode days by cost-sensitive neural network methods, Science of the Total Environment, 407, 2124, 10.1016/j.scitotenv.2008.12.007 · DOI
- Turney, 1995, Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2, 369, 10.1613/jair.120 · DOI · Paper page
- J. Van Hulse, T.M. Khoshgoftaar, A. Napolitano, An empirical comparison of repetitive undersampling techniques, in: Proceedings of the 2009 IEEE International Conference on Information Reuse, Integration (IRI’09), 2009, pp. 29–34. · DOI
- B.X. Wang, N. Japkowicz, Imbalanced data set learning with synthetic samples, in: Proceedings of the IRIS Machine Learning Workshop, 2004.
- Wang, 2012, Extract minimum positive and maximum negative features for imbalanced binary classification, Pattern Recognition, 45, 1136, 10.1016/j.patcog.2011.09.004 · doi:10.1016/j.patcog.2011.09.004
- S. Wang, X. Yao, Diversity analysis on imbalanced data sets by using ensemble models, in: Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM’09), 2009, pp. 324–331. · DOI · Paper page
- Wang, 2013, Relationships between diversity of classification ensembles and single-class performance measures, IEEE Transactions on Knowledge and Data Engineering, 25, 206, 10.1109/TKDE.2011.207 · DOI
- Wang, 2011, Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis, BMC Genomics, 12
- Wasikowski, 2010, Combating the small sample class imbalance problem using feature selection, IEEE Transactions on Knowledge and Data Engineering, 22, 1388, 10.1109/TKDE.2009.187 · DOI · Paper page
- Weiss, 1999, Timeweaver: a genetic algorithm for identifying pre-dictive patterns in sequences of events, vol. 1, 718
- Weiss, 2010, The impact of small disjuncts on classifier learning, vol. 8, 193
- Weiss, 2003, Learning when training data are costly: the effect of class distribution on tree induction, Journal of Artificial Intelligence Research, 19, 315, 10.1613/jair.1199 · DOI · Paper page
- Weiss, 2008, Maximizing classifier utility when there are data acquisition and modeling costs, Data Mining and Knowledge Discovery, 17, 253, 10.1007/s10618-007-0082-x · DOI
- Wilson, 1972, Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions on Systems, Man and Cybernetics, 2, 408, 10.1109/TSMC.1972.4309137 · DOI · Paper page
- R. Yan, Y. Liu, R. Jin, A. Hauptmann, On predicting rare classes with SVM ensembles in scene classification, in: Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’03), vol. 3, 2003, pp. 21–24. · DOI
- Yang, 2009, A particle swarm based hybrid system for imbalanced medical data sampling, BMC Genomics, 10
- Yang, 2006, 10 challenging problems in data mining research, International Journal of Information Technology and Decision Making, 5, 597, 10.1142/S0219622006002258 · DOI · Paper page
- Yang, 2008, Conceptual equivalence for contrast mining in classification learning, Data & Knowledge Engineering, 67, 413, 10.1016/j.datak.2008.07.001 · DOI
- S. Yen, Y. Lee, Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset, in: Proceedings of the 2006 International Conference on Intelligent, Computing (ICIC06), 2006, pp. 731–740. · doi:10.1007/978-3-540-37256-1_89 · Paper page
- K. Yoon, S. Kwek, An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics, in: Proceedings of the 5th International Conference on Hybrid Intelligent Systems (HIS’05), 2005, pp. 303–308.
- B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, in: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (KDD’01), 2001, pp. 204–213. · DOI · Paper page
- J. Zhang, I. Mani, KNN approach to unbalanced data distributions: a case study involving information extraction, in: Proceedings of the 20th International Conference on Machine Learning (ICML’03), Workshop Learning from Imbalanced Data Sets, 2003.
- Zhou, 2006, Training cost-sensitive neural networks with methods addressing the class imbalance problem, IEEE Transactions on Knowledge and Data Engineering, 18, 63, 10.1109/TKDE.2006.17 · DOI · Paper page
- Zhu, 2004, Class noise vs. attribute noise: a quantitative study, Artificial Intelligence Review, 22, 177, 10.1007/s10462-004-0751-8 · DOI · Paper page
- Zong, 2013, Weighted extreme learning machine for imbalance learning, Neurocomputing, 101, 229, 10.1016/j.neucom.2012.08.010 · DOI · Paper page
Cited by
Learning from imbalanced data: open challenges and future directions
Krawczyk, B. (2016) · Progress in Artificial Intelligence · doi:10.1007/s13748-016-0094-0 · Cited by 2,459
Classification assessment methods
Tharwat, A. (2021) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.08.003 · Cited by 2,417
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., & Bing, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,377
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
Big data preprocessing: methods and prospects
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
Self-citation
Metadata
- Authors
- López, V., Fernández, A., García, S., Palade, V., & Herrera, F.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2013-11
- Volume
- 250
- Pages
- 113-141
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2164330572
- Cited by
- 1,590
- References
- 11
- FWCI
- 72.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Ministerio de Ciencia y Tecnología (TIN2011-28488)
- Ministerio de Educación, Cultura y Deporte
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. https://app.cochat.ai/papers/10.1016/j.ins.2013.07.007
- The paper
- López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences. https://doi.org/10.1016/j.ins.2013.07.007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.