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Learning from imbalanced data
Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study [PeerJ]
Classification of imbalanced cloud image data using deep neural networks: performance improvement through a data science competition | Progress in Earth and Planetary Science | Full Text
Learning from imbalanced data
PDF] Learning from Imbalanced Data | Semantic Scholar
PDF) Ensemble Learning For Imbalanced Data Classification Problem
Frontiers | Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy
A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
PDF) Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach
Imbalanced data preprocessing techniques for machine learning: a systematic mapping study | SpringerLink
Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System | Industrial & Engineering Chemistry Research
Information | Free Full-Text | Learning from High-Dimensional and Class-Imbalanced Datasets Using Random Forests
PDF) Training deep neural networks on imbalanced data sets
KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods | SpringerLink
IMBALANCED DATASET CLASSIFICATION AND SOLUTIONS : A REVIEW | Semantic Scholar
Research on expansion and classification of imbalanced data based on SMOTE algorithm | Scientific Reports
GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning | Journal of Chemical Information and Modeling