Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Improved Sampling Techniques for Learning an Imbalanced Data Set
Learning from Imbalanced Data
The class imbalance problem in pattern classification and learning
Dynamic Sampling in Convolutional Neural Networks for Imbalanced Data Classification
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Ensemble Strategy for Hard Classifying Samples in Class-Imbalanced Data Set
Cost-Sensitive Learning and the Class Imbalance Problem
The class imbalance problem: A systematic study
DATA MINING FOR IMBALANCED DATASETS: AN OVERVIEW
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Data mining with imbalanced class distributions: concepts and methods.
Weighted extreme learning machine for imbalance learning
Random Balance : Ensembles of variable priors classifiers for imbalanced data
Local neighbourhood extension of SMOTE for mining imbalanced data
Clustering-based undersampling in class-imbalanced data
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
A review of boosting methods for imbalanced data classification
Handling imbalanced datasets: A review
Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
Inductive learning algorithms and representations for text categorization
RFM-SLAM: Exploiting relative feature measurements to separate orientation and position estimation in SLAM
how to attain value for money : comparing ppp and traditional infrastructure public procurement by .
Reading Digits in Natural Images with Unsupervised Feature Learning
A Vision Approach for Expiry Date Recognition using Stretched Gabor Features