Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Local reweight wrapper for the problem of imbalance
International Journal of Artificial Intelligence and Soft Computing
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An empirical comparison of repetitive undersampling techniques
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An asymmetric classifier based on partial least squares
Pattern Recognition
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
An empirical study of applying ensembles of heterogeneous classifiers on imperfect data
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Expert Systems with Applications: An International Journal
Borderline over-sampling for imbalanced data classification
International Journal of Knowledge Engineering and Soft Data Paradigms
Iranian cancer patient detection using a new method for learning at imbalanced datasets
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Detection of cancer patients using an innovative method for learning at imbalanced datasets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Clustering based bagging algorithm on imbalanced data sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Generating diverse ensembles to counter the problem of class imbalance
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A novel synthetic minority oversampling technique for imbalanced data set learning
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A measure oriented training scheme for imbalanced classification problems
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Prediction of liquefaction potential based on CPT up-sampling
Computers & Geosciences
An integrated data mining approach to real-time clinical monitoring and deterioration warning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical study of learning from imbalanced data
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
A new probabilistic active sample selection algorithm for class imbalance problem
International Journal of Knowledge Engineering and Soft Data Paradigms
A feature-word-topic model for image annotation and retrieval
ACM Transactions on the Web (TWEB)
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
Multimedia Tools and Applications
Imbalanced evolving self-organizing learning
Neurocomputing
Irrelevant attributes and imbalanced classes in multi-label text-categorization domains
Intelligent Data Analysis
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Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class examples are ignored. We propose two algorithms to overcome the deficiency. EasyEnsemble samples several subsets from the major class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade is similar to EasyEnsemble except that it removes correctly classified major class examples of trained learners from further consideration. Experiments show that both of the proposed algorithms have better AUC scores than many existing class-imbalance learning methods. Moreover, they have approximately the same training time as that of under-sampling, which trains significantly faster than other methods.