Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: MUTE: Majority under-sampling technique
SMOTE: Synthetic Minority Over-sampling Technique
Cluster-based sampling of multiclass imbalanced data
Evolutionary undersampling for extremely imbalanced big data classification under apache spark
Local neighbourhood extension of SMOTE for mining imbalanced data
DATA MINING FOR IMBALANCED DATASETS: AN OVERVIEW
Improved Consistent Sampling, Weighted Minhash and L1 Sketching
Survey of resampling techniques for improving classification performance in unbalanced datasets
Automatic Determination of Neighborhood Size in SMOTE
Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Data mining with imbalanced class distributions: concepts and methods.
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
Balanced undersampling: a novel sentence-based undersampling method to improve recognition of named entities in chemical and biomedical text
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Binarization With Boosting and Oversampling for Multiclass Classification
Improved ensemble learning for classification techniques based on majority voting
On the Class Imbalance Problem
borderline - smote : a new over - sampling method in imbalanced data sets learning .
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning
Designing mobile interfaces for novice and low-literacy users
C4.5: Programs for Machine Learning
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
A Novel Technique for Detecting Plagiarism in Documents Exploiting Information Sources
Automated Scoring: Beyond Natural Language Processing