Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Hybrid sampling for imbalanced data
Experimental perspectives on learning from imbalanced data
Evolutionary undersampling for imbalanced big data classification
Learning Deep Representation for Imbalanced Classification
Feature Selection in Imbalance data sets
an efficient mechanism for classification of imbalanced big data .
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary
Hierarchical classification for dealing with the Class imbalance problem
Diversity analysis on imbalanced data sets by using ensemble models
CLASSIFICATION OF IMBALANCED DATA: A REVIEW
Empowering Imbalanced Data in Supervised Learning: A Semi-supervised Learning Approach
Progressive Boosting for Class Imbalance
Calibrating Probability with Undersampling for Unbalanced Classification
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
Exploring of clustering algorithm on class-imbalanced data
Imbalanced Data learning Approaches
Integrated Oversampling for Imbalanced Time Series Classification
Boosting support vector machines for imbalanced data sets
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
Classification of Imbalance Data using Tomek Link(T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method
50GHz Double-Balanced Up-Conversion Mixer Using CMOS 90nm Process
C4.5: Programs for Machine Learning
On GANs and GMMs
Segmental acoustic indexing for zero resource keyword search
A Hybrid Method to Detect and Verify Vehicle Crash with Haar-Like Features and SVM Over the Web