Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
Efficient resampling methods for training support vector machines with imbalanced datasets
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Integrated Oversampling for Imbalanced Time Series Classification
Dealing with multiple classes in online class imbalance learning
SMOTE: Synthetic Minority Over-sampling Technique
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
Survey of resampling techniques for improving classification performance in unbalanced datasets
DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
RACOG and wRACOG: Two Probabilistic Oversampling Techniques
Progressive Boosting for Class Imbalance
SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling
Multiclass Imbalance Problems: Analysis and Potential Solutions
Ensemble Strategy for Hard Classifying Samples in Class-Imbalanced Data Set
Addressing imbalance in multilabel classification: Measures and random resampling algorithms
A review of boosting methods for imbalanced data classification
Local neighbourhood extension of SMOTE for mining imbalanced data
Clustering-based undersampling in class-imbalanced data
Distributional Random Oversampling for Imbalanced Text Classification
Sorting And Indexing With Partitioned B-Trees
Erasure Coding for Real-Time Streaming
Editorial: special issue on learning from imbalanced data sets
The Duke-UNC Functional Social Support Questionnaire. Measurement of social support in family medicine patients.
Safe use of mobile devices in the cyberspace