An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Stacking for Misclassification Cost Performance
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Predicting rare classes: can boosting make any weak learner strong?
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Linear Asymmetric Classifier for cascade detectors
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Boosted Classification Trees and Class Probability/Quantile Estimation
The Journal of Machine Learning Research
Sparse probabilistic classifiers
Proceedings of the 24th international conference on Machine learning
Proceedings of the 24th international conference on Machine learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Multi-class cost-sensitive boosting with p-norm loss functions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Cost-sensitive boosting neural networks for software defect prediction
Expert Systems with Applications: An International Journal
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
Making class bias useful: a strategy of learning from imbalanced data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Maximal-margin approach for cost-sensitive learning based on scaled convex hull
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Frequent subsequence-based protein localization
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Shedding light on the asymmetric learning capability of AdaBoost
Pattern Recognition Letters
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Parameter inference of cost-sensitive boosting algorithms
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Double-base asymmetric AdaBoost
Neurocomputing
Imbalanced evolving self-organizing learning
Neurocomputing
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