Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper.
Query: of the Thesis Classification of Imbalanced Data Using Synthetic OverSampling Techniques by
Borderline over-sampling for imbalanced data classification
Hybrid sampling for imbalanced data
Survey of resampling techniques for improving classification performance in unbalanced datasets
Coupling different methods for overcoming the class imbalance problem
Local neighbourhood extension of SMOTE for mining imbalanced data
An overview of classification algorithms for imbalanced datasets
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Distributional Random Oversampling for Imbalanced Text Classification
The class imbalance problem: A systematic study
Learning from Imbalanced Data
An insight into classification with imbalanced data : Empirical results and current trends on using data intrinsic characteristics
the effect of oversampling and undersampling on classifying imbalanced text datasets .
Experimental perspectives on learning from imbalanced data
Transfer learning for class imbalance problems with inadequate data
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
Handling imbalanced datasets: A review
Learning from imbalanced data: open challenges and future directions
Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM
Cluster-based sampling of multiclass imbalanced data
Applying support vector machines to imbalanced datasets
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Distributional vectors encode referential attributes
The MOND limit from space-time scale invariance
User interest prediction over future unobserved topics on social networks