Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Imbalanced Data learning Approaches
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
Learning Decision Trees for Unbalanced Data
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Ensemble Strategy for Hard Classifying Samples in Class-Imbalanced Data Set
A Nonuniform Weighted Loss Function for Imbalanced Image Classification
Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification
Adaptive weighted imbalance learning with application to abnormal activity recognition
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling
DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification
Borderline over-sampling for imbalanced data classification
Adaptive Learning-Based $k$ -Nearest Neighbor Classifiers With Resilience to Class Imbalance
RHSBoost: Improving classification performance in imbalance data
Training cost-sensitive neural networks with methods addressing the class imbalance problem
An unsupervised self-organizing learning with support vector ranking for imbalanced datasets
Classifying imbalanced data sets using similarity based hierarchical decomposition
Hierarchical classification for dealing with the Class imbalance problem
A Survey of Predictive Modeling on Imbalanced Domains
Cluster-based sampling of multiclass imbalanced data
Improving Academic Performance Prediction by Dealing with Class Imbalance
A FVF LDO Regulator With Dual-Summed Miller Frequency Compensation for Wide Load Capacitance Range Applications
Incidence Estimate of Nonmelanoma Skin Cancer in the United States, 2006
T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS
Diffractive and holographic optics as optical combiners in head mounted displays
The Case against Accuracy Estimation for Comparing Induction Algorithms