Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: An Optimized Cost-Sensitive SVM for Imbalanced Data Learning
Cost-sensitive learning methods for imbalanced data
SVMs Modeling for Highly Imbalanced Classification
Improved Sampling Techniques for Learning an Imbalanced Data Set
Revisiting AdaBoost for Cost-Sensitive Classification. Part II: Empirical Analysis.
Cost-Sensitive Semi-Supervised Support Vector Machine
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Imbalanced Data learning Approaches
The class imbalance problem in pattern classification and learning
Learning from imbalanced data: open challenges and future directions
A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios
Weighted extreme learning machine for imbalance learning
Multi-class cost-sensitive boosting with p-norm loss functions
BRACID: a comprehensive approach to learning rules from imbalanced data
Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost
Efficient resampling methods for training support vector machines with imbalanced datasets
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Local neighbourhood extension of SMOTE for mining imbalanced data
A review of boosting methods for imbalanced data classification
AdaCost: Misclassification Cost-Sensitive Boosting
The class imbalance problem: A systematic study
Boosting and Rocchio applied to text filtering
Interactive machine learning
An invitation to reproducible computational research.
Scalable distributed DNN training using commodity GPU cloud computing.
The Design of the Borealis Stream Processing Engine