Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper.
Query: Boosting support vector machines for imbalanced data sets
Applying support vector machines to imbalanced datasets
An overview of classification algorithms for imbalanced datasets
Local neighbourhood extension of SMOTE for mining imbalanced data
Experimental perspectives on learning from imbalanced data
Coupling different methods for overcoming the class imbalance problem
Feature Selection in Imbalance data sets
Cost-sensitive learning methods for imbalanced data
Multiclass Imbalance Problems: Analysis and Potential Solutions
Learning from imbalanced data: open challenges and future directions
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Improving the Accuracy and Speed of Support Vector Machines
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
an efficient mechanism for classification of imbalanced big data .
Ensemble Strategy for Hard Classifying Samples in Class-Imbalanced Data Set
machine learning from imbalanced data sets 101 extended .
Random Balance : Ensembles of variable priors classifiers for imbalanced data
Data Augmentation for Support Vector Machines
Transfer learning for class imbalance problems with inadequate data
RUSBoost: Improving classification performance when training data is skewed
An empirical study of tokenization strategies for biomedical information retrieval
BGP Anomaly Detection Techniques: A Survey
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
Cadaveric Simulation of a Pes Cavus Foot
Efficient frame-sequential label propagation for video object segmentation