Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: SMOTEBoost: Improving Prediction of the Minority Class in Boosting
Evaluating boosting algorithms to classify rare classes: comparison and improvements
Boosting SVM classifiers by ensemble
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Boosting support vector machines for imbalanced data sets
Online bagging and boosting
SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification
E-commerce customer churn prediction based on improved SMOTE and AdaBoost
Roughly Balanced Bagging for Imbalanced Data
CGMOS: Certainty Guided Minority OverSampling
DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification
Self Supervised Boosting
AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets
AdaCost: Misclassification Cost-Sensitive Boosting
Query Learning Strategies Using Boosting and Bagging
Boosting and Other Ensemble Methods
Building Decision Trees for the Multi-class Imbalance Problem
A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.
Bagging, Boosting, and C4.5
Weighted Sampling for Large-Scale Boosting
An Efficient Boosting Algorithm for Combining Preferences
Tracing the Dynamic Changes in Perceived Tonal Organization in a Spatial Representation of Musical Keys
C4.5: Programs for Machine Learning
Joint scheduling and power control for wireless ad hoc networks
Patient understanding of food labels: the role of literacy and numeracy.
Low Power High Speed 3-2 Compressor