Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Types of minority class examples and their influence on learning classifiers from imbalanced data
Learning from Imbalanced Data
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Transfer learning for class imbalance problems with inadequate data
On the Class Imbalance Problem
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
A multiple resampling method for learning from imbalanced data sets
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
Adaptive Learning-Based $k$ -Nearest Neighbor Classifiers With Resilience to Class Imbalance
A review of boosting methods for imbalanced data classification
Hierarchical classification for dealing with the Class imbalance problem
Learning Deep Representation for Imbalanced Classification
Class imbalance and the curse of minority hubs
Cluster-based sampling of multiclass imbalanced data
Using Random Forest to Learn Imbalanced Data
OAHO: an Effective Algorithm for Multi-Class Learning from Imbalanced Data
Addressing imbalance in multilabel classification: Measures and random resampling algorithms
Handling imbalanced datasets: A review
Max-margin Class Imbalanced Learning with Gaussian Affinity
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
Survey of data-selection methods in statistical machine translation
SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Effects of Intensive Phonomotor Treatment on Reading in Eight Individuals With Aphasia and Phonological Alexia
Deep Sequential Neural Network
TTS Synthesis with Bidirectional LSTM based Recurrent Neural Networks