Journal Contributions: Published

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        2009  2008  2007  2006  2005  2004  2003  2002  2001  2000
        1998  1997

2009
	J. Burez, D. Van den Poel. Handling class imbalance in customer churn prediction. Expert Systems with Applications 36 (2009) 4626-4636			bib
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	S.-J. Yen, Y.-S. Lee. Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications 36 (2009) 5718-5727			bib
2008
	M.-C. Chen, L.-S. Chen, C.-C. Hsu, W.-R. Zeng. An information granulation based data mining approach for classifying imbalanced data. Information Sciences 178:16 (2008) 3214-3227			bib
	B.H. Cho, H. Yu, K.-W. Kim, T.H. Kim, I.-Y. Kim, S.I. Kim. Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine 42:1 (2008) 37-53			bib
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	D.-F. Li, W.-C. Hu, W. Xiong, J.-B. Yang. Fuzzy relevance vector machine for learning from unbalanced data and noise. Pattern Recognition Letters 29:9 (2008) 1175-1181			bib
	T.W. Liao. Classification of weld flaws with imbalanced class data. Expert Systems with Applications 35:3 (2008) 1041-1052			bib
	J. Liu, Q. Hu, D. Yu. A weighted rough set based method developed for class imbalance learning. Information Sciences 178:4 (2008) 1235-1256			bib
	J. Liu, Q. Hu, D. Yu. A comparative study on rough set based class imbalance learning. Knowledge Based Systems 21:8 (2008) 753-763			bib
	J.-H. Xue, D.M. Titterington. Do unbalanced data have a negative effect on LDA?. Pattern Recognition 41:5 (2008) 1558-1571			bib
	X.-M. Zhao, X. Li, K. Aihara. Protein classification with imbalanced data. Proteins 70:4 (2008) 1125-1132			bib
2007
	H. Ahn, H. Moon, M.J. Fazzari, N. Lim, J.J. Chen, R.L. Kodell. Classification by ensembles from random partitions of high-dimensional data. Computational Statistics and Data Analysis 51:12 (2007) 6166-6179			bib
	T. Eitrich, A. Kless, C. Druska, W. Meyer, J. Grotendorst. Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques. Journal of Chemical Information and Modeling 47:1 (2007) 92-103			bib
	X. Hong, S. Chen, C.J. Harris. A kernel-based two-class classifier for imbalanced data sets. IEEE Transactions on Neural Networks 18:1 (2007) 28-41			bib
	B. Lerner, J. Yeshaya, L. Koushnir. On the classification of a small imbalanced cytogenetic image database. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4:2 (2007) 204-215			bib
	A.B. Owen. Infinitely imbalanced logistic regression. Journal of Machine Learning Research 8 (2007) 761-773			bib
	C.-T. Su, Y.-H. Hsiao. An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge and Data Engineering 19:10 (2007) 1321-1332			bib
	Y. Sun, M.S. Kamel, A.K.C. Wong, Y. Wang. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40:12 (2007) 3358-3378			bib
	J. Xie, Z. Qiu. The effect of imbalanced data sets on LDA: A theoretical and empirical analysis. Pattern Recognition 40:2 (2007) 557-562			bib
	L. Xu, M.-Y. Chow, L.S. Taylor. Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification E-Algorithm. IEEE Transactions on Power Systems 22:1 (2007) 164-171			bib
	L. Xu, M.-Y. Chow, J. Timmis, L.S. Taylor. Power distribution outage cause identification with imbalanced data using Artificial Immune Recognition System (AIRS) algorithm. IEEE Transactions on Power Systems 22:1 (2007) 198-204			bib
	K. Yoon, S. Kwek. A data reduction approach for resolving the imbalanced data issue in functional genomics. Neural Computing and Applications 16:3 (2007) 295-306			bib
2006
	Y. Liu, N.V. Chawla, M.P. Harper, E. Shriberg, A. Stolcke. A study in machine learning from imbalanced data for sentence boundary detection in speech. Computer Speech and Language 20:4 (2006) 468-494			bib
	H. Shin, S.B. Cho. Response modeling with support vector machines. Expert Systems with Applications 30:4 (2006) 746-760			bib
	C.T. Su, L.S. Chen, T.L. Chiang. A neural network based information granulation approach to shorten the cellular phone test process. Computers in Industry 57:5 (2006) 412-423			bib
	Z.H. Zhou, X.Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18:1 (2006) 63-77			bib
2005
	P. Campadelli, E. Casiraghi, G. Valentini. Support vector machines for candidate nodules classification. Neurocomputing 68 (2005) 281-288			bib
	G.M. Fung, O.L. Mangasarian. Multicategory proximal support vector machine classifiers. Machine Learning 59:1-2 (2005) 77-97			bib
	S. Tan. Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications 28:4 (2005) 667-671			bib
	G. Wu, E.Y. Chang. KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Transactions on Knowledge and Data Engineering 17:6 (2005) 786-795			bib
2004
	G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29			bib
	N.V. Chawla, N. Japkowicz, A. Kolcz. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations 6:1 (2004) 1-6			bib
	M.D. del Castillo, J.I. Serrano. A multistrategy approach for digital text categorization from imbalanced documents. SIGKDD Explorations 6:1 (2004) 70-79			bib
	H. Guo, H.L. Viktor. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. SIGKDD Explorations 6:1 (2004) 30-39			bib
	T. Jo, N. Japkowicz. Class imbalances versus small disjuncts. SIGKDD Explorations 6:1 (2004) 40-49			bib
	Y.L. Murphey, H. Guo, L.A. Feldkamp. Neural learning from unbalanced data. Applied Intelligence 21:2 (2004) 117-128			bib
	C. Phua, D. Alahakoon, V. Lee. Minority report in fraud detection: classification of skewed data. SIGKDD Explorations 6:1 (2004) 50-59			bib
	P. Radivojac, N.V. Chawla, A.K. Dunker, Z. Obradovic. Classification and knowledge discovery in protein databases. Journal of Biomedical Informatics 37:4 (2004) 224-239			bib
	B. Raskutti, A. Kowalczyk. Extreme re-balancing for SVMs: a case study. SIGKDD Explorations 6:1 (2004) 60-69			bib
	G.M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explorations 6:1 (2004) 7-19			bib
	Z. Zheng, X. Wu, R.K. Srihari. Feature selection for text categorization on imbalanced data. SIGKDD Explorations 6:1 (2004) 80-89			bib
2003
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	D.J. Hand, V. Vinciotti. Choosing k for two-class nearest neighbour classifiers with unbalanced classes. Pattern Recognition Letters 24:9-10 (2003) 1555-1562			bib
	S. Kotsiantis, P. Pintelas. Mixture of expert agents for handling imbalanced data sets. Annals of Mathematics, Computing & TeleInformatics 1:1 (2003) 46-55			bib
	S. Merler, C. Furlanello, B. Larcher, A. Sboner. Automatic model selection in cost-sensitive boosting. Information Fusion 4:1 (2003) 3-10			bib
	G.M. Weiss, F.J. Provost. Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19 (2003) 315-354			bib
2002
	N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16 (2002) 321-357			bib
	N. Japkowicz, S. Stephen. The class imbalance problem: a systematic study. Intelligent Data Analysis 6:5 (2002) 429-449			bib
2001
	G. Dupret, M. Koda. Bootstrap re-sampling for unbalanced data in supervised learning. European Journal of Operational Research 134:1 (2001) 141-156			bib
	N. Japkowicz. Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning 42:1-2 (2001) 97-122			bib
	F.J. Provost, T. Fawcett. Robust classification for imprecise environments. Machine Learning 42:3 (2001) 203-231			bib
2000
	S.S. Lee. Noisy replication in skewed binary classification. Computational Statistics & Data Analysis 34:2 (2000) 165-191			bib
1998
	M. Kubat, R.C. Holte, S. Matwin. Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30:2-3 (1998) 195-215			bib
	S. Ramanan, T.G. Clarkson, J.G. Taylor. Adaptive algorithm for training pRAM neural networks on unbalanced data sets. Electronics Letters 34:13 (1998) 1335-1336			bib
1997
	L. Bruzzone, S.B. Serpico. Classification of imbalanced remote-sensing data by neural networks. Pattern Recognition Letters 18:11 (1997) 1323-1328			bib

 Contributions to Books Chapters

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1996
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 Conference Contributions

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        1998  1997  1996

2008
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	D.A. Cieslak, N.V. Chawla. Learning decision trees for unbalanced data. European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD08). Antwerp (Belgium, 2008) 241-256			bib
	J. Doucette, M.I. Heywood. GP classification under imbalanced data sets: active sub-sampling and AUC approximation. 11th European Conference on Genetic Programming (EuroGP08). Naples (Italy, 2008) 266-277			bib
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	S. Gazzah, N.E.B. Amara. New oversampling approaches based on polynomial fitting for imbalanced data sets. The Eighth IAPR International Workshop on Document Analysis Systems (DAS08). (2008) 677-684			bib
	X. Guo, Y. Yin, C. Dong, G. Yang, G. Zhou. On the class imbalance problem. Fourth International Conference on Natural Computation (ICNC08). (2008) 192-201			bib
	S. Hido, H. Kashima:. Roughly balanced bagging for imbalanced data. SIAM International Conference on Data Mining (SDM08). Atlanta (USA, 2008) 143-152			bib
	P. Lenca, S. Lallich, T.-N. Do, N.-K. Pham. A comparison of different off-centered entropies to deal with class imbalance for decision trees. 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD08). Osaka (Japan, 2008) 634-643			bib
	P. Li, P.-L. Qiao, Y.-C. Liu. A hybrid re-sampling method for svm learning from imbalanced data sets. Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD08). Shandong (China, 2008) 65-69			bib
	Z. Lu, A.I. Rughani, B.I. Tranmer, J. Bongard. Informative sampling for large unbalanced data sets. Genetic and Evolutionary Computation Conference (GECCO08). Atlanta (USA, 2008) 2047-2054			bib
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	N. Seliya, Z. Xu, T.M. Khoshgoftaar. Addressing class imbalance in non-binary classification problems. 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI08). Dayton (USA, 2008) 460-466			bib
	J. Stefanowski, S. Wilk. Selective pre-processing of imbalanced data for improving classification performance. 10th International Conference on Data Warehousing and Knowledge Discovery (DaWaK08). Turin (Italy, 2008) 283-292			bib
	G. Tepvorachai, C.A. Papachristou. Multi-label imbalanced data enrichment process in neural net classifier training. International Joint Conference on Neural Networks (IJCNN08). Hong Kong (China, 2008) 1301-1307			bib
	D. Ye, Z. Chen. A rough set based minority class oriented learning algorithm for highly unbalanced data sets. IEEE International Conference on Granular Computing (GrC08). (2008) 736-739			bib
	Y. Zhang, B. Luo. Parallel classifiers ensemble with hierarchical machine learning for imbalanced classes. International Conference on Machine Learning and Cybernetics (ICMLC08). Kunming (China, 2008) 94-99			bib
	S. Zou, Y. Huang, Y. Wang, J. Wang, C. Zhou. SVM learning from imbalanced data by GA sampling for protein domain prediction. 9th International Conference for Young Computer Scientists (ICYCS08). Zhang Jia Jie (China, 2008) 982-987			bib
2007
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	M. Beigi, A. Zell. Synthetic protein sequence oversampling method for classification and remote homology detection in imbalanced protein data. 1st International Conference on Bioinformatics Research and Development (BIRD07). Berlin (Germany, 2007) 263-277			bib
	X. Hao, X. Tao, C. Zhang, Y. Hu. An effective method to improve kNN text classifier. 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD07). Qingdao (China, 2007) 379-384			bib
	L. Pelayo, S. Dick. Applying novel resampling strategies to software defect prediction. Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS07). (2007) 69-72			bib
	J. Van Hulse, T.M. Khoshgoftaar, A. Napolitano. Experimental perspectives on learning from imbalanced data. 24th International Conference on Machine Learning (ICML07). Corvalis (USA, 2007) 935-942			bib
2006
	B. Arunasalam, S. Chawla. CCCS: a top-down associative classifier for imbalanced class distribution. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (No Data). ACM. Philadelphia (USA, 2006) 517-522			bib
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	H.-j. Lee, S. Cho. The novelty detection approach for different degrees of class imbalance. 13th International Conference on Neural Information Processing. Lecture Notes in Computer Science 4233, Springer 2006, Hong Kong (China, 2006) 21-30			bib
	Y. Liu, A. An, X. Huang. Boosting prediction accuracy on imbalanced datasets with SVM ensembles. 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD06). Lecture Notes in Computer Science 3918, Springer 2006 (Singapore, 2006) 107-118			bib
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	S. Pang, I. Havukkala, N. Kasabov. Two-class SVM trees (2-SVMT) for biomarker data analysis. 3rd International Symposium on Neural Networks (ISNN06). Lecture Notes in Computer Science 3973, Springer 2006, Chengdu (China, 2006) 629-634			bib
	Y. Sun, M.S. Kamel, Y. Wang. Boosting for learning multiple classes with imbalances class distribution. 6th IEEE International Conference on Data Mining (ICDM06). Hong Kong (China, 2006) 592-602			bib
	J. Wang, M. Xu, H. Wang, J. Zhang. Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding. 8th International Conference on Signal Processing (No Data). IEEE. No Data (No Data, 2006) 0-0			bib
	S.-J. Yen, Y.-S. Lee. Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset. International Conference on Intelligent Computing (No Data). Springer. No Data (No Data, 2006) 731-740			bib
	S.-J. Yen, Y.-S. Lee, C.-H. Lin, J.-C. Ying. Investigating the effect of sampling methods for imbalanced data distributions. IEEE International Conference on Systems, Man and Cybernetics (ICSMC06). (2006) 4163-4168			bib
2005
	X.W. Chen, B. Gerlach, D. Casasent. Pruning support vectors for imbalanced data classification. International Joint Conference on Neural Networks (IJCNN05). IEEE. Montreal (Canada, 2005) 1883-1888			bib
	C. Drummond, R.C. Holte. Severe class imbalance: why better algorithms aren't the answer. 16th European Conference on Machine Learning (ECML05). Lecture Notes in Computer Science 3720, Springer 2005, Porto (Portugal, 2005) 539-546			bib
	T. Eitrich, B. Lang. Parallel tuning of support vector machine learning parameters for large and unbalanced data sets. Ist International Symposium on Computational Life Sciences (CompLife'05). Lecture Notes in Computer Science 3695, Springer-Verlag 2005, Konstanz (Germany, 2005) 253-264			bib
	H. Han, W.Y. Wang, B.H. Mao. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. International Conference on Intelligent Computing (ICIC'05). Lecture Notes in Computer Science 3644, Springer-Verlag 2005, Hefei (China, 2005) 878-887			bib
	C.H. Nguyen, T.B. Ho. An imbalanced data rule learner. 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD05). Lecture Notes in Computer Science 3721, Springer 2005, Porto (Portugal, 2005) 617-624			bib
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	E. Scheurmann, C. Matthews. Neural network classifers in arrears management. XVth International Conference on Artificial Neural Networks: Formal Models and Their Applications (ICANN'05). Lecture Notes in Computer Science 3697, Springer-Verlag 2005, Warsaw (Poland, 2005) 325-330			bib
	Y. Sun, A.K.C. Wong, Y. Wang. Parameter inference of cost-sensitive boosting algorithms. IVth International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM'05). Lecture Notes in Computer Science 3587, Springer-Verlag 2005, Leipzig (Germany, 2005) 21-30			bib
	S. Visa, A. Ralescu. The effect of imbalanced data class distribution on fuzzy classifiers - experimental study. XIVth IEEE International Conference on Fuzzy Systems (FUZZ'05). Reno (USA, 2005) 749-754			bib
	K. Yoon, S. Kwek. An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics. 5th International Conference on Hybrid Intelligent Systems (HIS05). IEEE. Rio de Janeiro (Brazil, 2005) 303-308			bib
	D. Zhuang, B. Zhang, Q. Yang, J. Yan, Z. Chen, Y. Chen. Efficient text classification by weighted proximal SVM. 5th IEEE International Conference on Data Mining (ICDM05). IEEE. Houston (USA, 2005) 538-545			bib
	L. Zhuang, H. Dai, X. Hang. A novel field learning algorithm for dual imbalance text classification. IInd International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'05). Lecture Notes in Computer Science 3614, Springer-Verlag 2005, Changsha (China, 2005) 39-48			bib
2004
	R. Akbani, S. Kwek, N. Japkowicz. Applying support vector machines to imbalanced datasets. XVth European Conference on Machine Learning (ECML'04). Lecture Notes in Computer Science 3201, Springer-Verlag 2004, Pisa (Italy, 2004) 39-50			bib
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	J.X. Chen, T.H. Cheng, A.L.F. Chan, H.Y. Wang. An application of classification analysis for skewed class distribution in therapeutic drug monitoring - the case of vancomycin. Workshop on Medical Information Systems (IDEAS-DH'04). IEEE. Beijing (China, 2004) 35-39			bib
	G. Cohen, M. Hilario, C. Pellegrini. One-class support vector machines with a conformal kernel. A case study in handling class imbalance. Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR/SPR'04). Lecture Notes in Computer Science 3138, Springer-Verlag 2004, Lisbon (Portugal, 2004) 850-858			bib
	J.W. Grzymala-Busse, J. Stefanowski, S. Wilk. A comparison of two approaches to data mining from imbalanced data. VIIIth International ConferenceKnowledge-Based Intelligent Information and Engineering Systems (KES'04). Lecture Notes in Computer Science 3213, Springer-Verlag 2004, Wellington (New Zealand, 2004) 757-763			bib
	K. Huang, H. Yang, I. King, M.R. Lyu. Learning classifiers from imbalanced data based on biased minimax probability machine. 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04). Washington DC (USA, 2004) 558-563			bib
	M. Muhlbaier, A. Topalis, R. Polikar. Incremental learning from unbalanced data. IEEE International Joint Conference on Neural Networks (ICJNN'04). Budapest (Hungary, 2004) 1057-1062			bib
	D.L. Olson. Data Set Balancing. Chinese Academy of Sciences Symposium on Data Mining and Knowledge Management (CASDMKM'04). Lecture Notes in Computer Science 3327, Springer-Verlag 2004, Beijing (China, 2004) 71-80			bib
	R.C. Prati, G.E.A.P.A. Batista, M.C. Monard. Learning with class skews and small disjuncts. XVIIth Brazilian Symposium on Artificial Intelligence (SBIA'04). Lecture Notes in Computer Science 3171, Springer-Verlag 2004, Sao Luis (Brazil, 2004) 296-306			bib
	R.C. Prati, G.E.A.P.A. Batista, M.C. Monard. Class imbalances versus class overlapping: an analysis of a learning system behavior. III Mexican International Conference on Artificial Intelligence (MICAI'04). Lecture Notes in Computer Science 2972, Springer-Verlag 2004, Mexico City (Mexico, 2004) 312-321			bib
	J. Zhang, E. Bloedorn, L. Rosen, D. Venese. Learning rules from highly unbalanced data sets. IVth IEEE International Conference on Data Mining (ICDM'04). Brighton (UK, 2004) 571-574			bib
2003
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	H. Bian, L. Mazlack. Fuzzy-rough nearest-neighbor classification approach. XXIInd International Conference of the North American Fuzzy Information Processing Society (NAFIPS'03). Chicago (USA, 2003) 500-505			bib
	N.V. Chawla, A. Lazarevic, L.O. Hall, K.W. Bowyer. SMOTEBoost: improving prediction of the minority class in boosting. Knowledge Discovery in Databases. VIIth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'03). Lecture Notes in Computer Science 2838, Springer-Verlag 2003, Cavtat Dubrovnik (Croatia, 2003) 107-119			bib
	N.V. Chawla. C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	G. Cohen, M. Hilario, H. Sax, S. Hugonnet. Data imbalance in surveillance of nosocomial infections. IVth International Symposium on Medical Data Analysis (ISMDA'03). Lecture Notes in Computer Science 2868, Springer-Verlag 2003, Berlin (Germany, 2003) 109-117			bib
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	C. Drummond, R.C. Holte. C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	R. Hickey. Learning rare class footprints: the REFLEX algorithm. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	M.A. Maloof. Learning when data sets are imbalanced and when costs are unequal and unknown. Workshop on Learning from Imbalanced Data Sets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	P. Radivojac, U. Korad, K.M. Sivalingam, Z. Obradovic. Learning from class-imbalanced data in wireless sensor networks. LVIII Vehicular Technology Conference (VTC'03-Fall). IEEE. Orlando (USA, 2003) 3030-3034			bib
	S. Visa, A. Ralescu. Learning imbalanced and overlapping classes using fuzzy sets. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	G. Wu, E.Y. Chang. Class-boundary alignment for imbalanced dataset learning. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	J. Zhang, I. Mani. kNN approach to unbalanced data distributions: a case study involving information extraction. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
	Z. Zheng, R.K. Srihari. Optimally combining positive and negative features for text categorization. Workshop on Learning from Imbalanced Datasets (ICML'03). Washington DC (USA, 2003) 0-0			bib
2002
	X. Fu, L. Wang, K.S. Chua, F. Chu. Training RBF neural networks on unbalanced data. IX International Conference on Neural Information Processing (ICONIP'02). Singapore (Republic of Singapore, 2002) 1016-1020			bib
	M.V. Joshi, R.C. Agarwal, V. Kumar. Predicting rare classes: can boosting make any weak learner strong?. VIIIth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02). Edmonton (Canada, 2002) 297-306			bib
	M.C. Monard, G.E.A.P.A. Batista. Learning with skewed class distributions. Advances in Logic, Artificial Intelligence and Robotics (LAPTEC'02). Sao Paulo (Brazil, 2002) 173-180			bib
	A.T. Quang, Q.L. Zhang, X. Li. Evolving support vector machine parameters. International Conference on Machine Learning and Cybernetics (ICMLC'02). IEEE. Beijing (China, 2002) 548-551			bib
2001
	A. An, N. Cercone, X. Huang. A case study for learning from imbalanced data sets. XIV Biennial Conference of the Canadian Society for Computational Studies of Intelligence (AI'01). Lecture Notes in Computer Science 2056, Springer-Verlag 2001, Ottawa (Canada, 2001) 1-15			bib
	A. An, Y. Wang. Comparisons of classification methods for screening potential compounds. IEEE International Conference on Data Mining (ICDM'01). San Jose (USA, 2001) 11-18			bib
	C. Elkan. The foundations of cost-sensitive learning. XVII International Joint Conference on Artificial Intelligence (IJCAI'01). Washington DC (USA, 2001) 973-978			bib
	A. Estabrooks, N. Japkowicz. A mixture-of-experts framework for learning from imbalanced data sets. IV International Conference Advances in Intelligent Data Analysis (IDA'01). Lecture Notes in Computer Science 2189, Springer-Verlag 2001, Cascais (Portugal, 2001) 34-43			bib
	L.O. Hall. Data mining from extreme data sets: very large and/or very skewed data sets. IEEE International Conference on Systems, Man, and Cybernetics (SMC'01). Tucson (USA, 2001) 2555-2555			bib
	N. Japkowicz. Concept-learning in the presence of between-class and within-class imbalances. XIV Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (AI'01). Lecture Notes in Computer Science 2056, Springer-Verlag 2001, Ottawa (Canada, 2001) 67-77			bib
	M.V. Joshi, V. Kumar, R.C. Agarwal. Evaluating boosting algorithms to classify rare classes: comparison and improvements. IEEE International Conference on Data Mining (ICDM'01). San Jose (USA, 2001) 257-264			bib
	M.V. Joshi, R.C. Agarwal, V. Kumar. Mining needles in a haystack: classifying rare classes via two-phase rule induction. Conference on Management of Data (SIGMOD'01). Santa Barbara (USA, 2001) 0-0			bib
	P. Latinne, M. Saerens, C. Decaestecker. Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: evidence from a multi-class problem in remote sensing. XVIII International Conference on Machine Learning (ICML'01). Williamstown (USA, 2001) 298-305			bib
	K.K. Lee, S.R. Gunn, C.J. Harris, P.A.S. Reed. Classification of imbalanced data with transparent kernels. International Joint Conference on Neural Networks (IJCNN'01). Washington DC (USA, 2001) 2410-2415			bib
	A. Nickerson, N. Japkowicz, E. Milios. Using unsupervised learning to guide re-sampling in imbalanced data sets. VIII International Workshop on AI and Statitsics (AIStats'01). Key West (USA, 2001) 261-265			bib
	B. Zadrozny, C. Elkan. Learning and making decisions when costs and probabilities are both unknown. VII ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'01). San Francisco (USA, 2001) 204-213			bib
2000
	C. Drummond, R.C. Holte. Exploiting the cost (in)sensitivity of decision tree splitting criteria. VII International Conference on Machine Learning (ICML'00). Standord (USA, 2000) 239-246			bib
	N. Japkowicz. Learning from imbalanced data sets: a comparison of various strategies. AAAI Workshop on Learning from Imbalanced Data Sets (AAAI'00). Austin (USA, 2000) 10-15			bib
	N. Japkowicz. The class imbalance problem: significance and strategies. International Conference on Artificial Intelligence (IC-AI'00). Las Vegas (USA, 2000) 111-117			bib
1999
	D. Carvalho, B. Avila, A.A. Freitas. A hybrid genetic algorithm/decision tree approach for coping with unbalanced classes. III International Conference on the Practical Applications of Knowledge Discovery and Data Mining (PADD'99). London (UK, 1999) 61-70			bib
	P. Domingos. MetaCost: a general method for making classifiers cost-sensitive. Vth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'99). San Diego (USA, 1999) 155-164			bib
	G. Dong, X. Zhang, L. Wong, J. Li. CAEP: Classification by Aggregating Emerging Patterns. IInd International Conference Discovery Science (DS'99). Lecture Notes in Computer Science 1721, Springer-Verlag 1999, Tokyo (Japan, 1999) 30-42			bib
	W. Fan, S.J. Stolfo, J. Zhang, P.K. Chan. AdaCost: misclassification cost-sensitive boosting. XVIth International Conference on Machine Learning (ICML'99). Bled (Slovenia, 1999) 97-105			bib
1998
	P.K. Chan, S.J. Stolfo. Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection. International Conference on Knowledge Discovery and Data Mining (KDD'98). New York City (USA, 1998) 164-168			bib
1997
	C. Cardie, N. Howe. Improving minority class prediction using case-specific feature weights. XIVth International Conference on Machine Learning (ICML'97). Nashville (USA, 1997) 57-65			bib
	M. Kubat, R.C. Holte, S. Matwin. Learning when negative examples abound. IX European Conference on Machine Learning (ECML'97). Lecture Notes in Computer Science 1224, Springer-Verlag 1997, Prague (Czech Republic, 1997) 146-153			bib
	M. Kubat, S. Matwin. Addressing the curse of imbalanced training sets: one-sided selection. XIV International Conference on Machine Learning (ICML'97). Nashville (USA, 1997) 179-186			bib
	F.J. Provost, T. Fawcett. Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. IIIrd International Conference on Knowledge Discovery and Data Mining (KDD'97). Newport Beach (USA, 1997) 43-48			bib
1996
	K.J. Ezawa, M. Singh, S.W. Norton. Learning goal oriented bayesian networks for telecommunications risk management. XIII International Conference on Machine Learning (ICML'96). Bari (Italy, 1996) 139-147			bib

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