Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data
Editorial: special issue on learning from imbalanced data sets
A review of boosting methods for imbalanced data classification
Data mining with imbalanced class distributions: concepts and methods.
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
Empowering Imbalanced Data in Supervised Learning: A Semi-supervised Learning Approach
A two-step supervised learning artificial neural network for imbalanced dataset problems
Using Random Forest to Learn Imbalanced Data
An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets
Hybrid sampling for imbalanced data
DATA MINING FOR IMBALANCED DATASETS: AN OVERVIEW
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Progressive Boosting for Class Imbalance
An insight into classification with imbalanced data : Empirical results and current trends on using data intrinsic characteristics
The Class Imbalance Problem: Significance and Strategies
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
Exploratory Under-Sampling for Class-Imbalance Learning
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
Extending Bagging for Imbalanced Data
Learning Deep Representation for Imbalanced Classification
Artificial Intelligence in Medicine
A Cascaded Multilevel Inverter Based on Switched-Capacitor for High-Frequency AC Power Distribution System
MISSL: multiple-instance semi-supervised learning
Towards night fog detection through use of in-vehicle multipurpose cameras
Using neural network ensembles for bankruptcy prediction and credit scoring