Sampling Methods<br>
Random Oversampling and Undersampling
Informed Undersampling
Synthetic Sampling with Data Generation
Sampling with Data Cleaning Techniques
Cluster-Based Sampling Method
Integration of Sampling and Boosting
Cost-Sensitive Learning Methods
Cost-Sensitive Dataspace Weighting with Adaptive Boosting
Cost-Sensitive Decision Trees
Cost-Sensitive Neural Networks
Kernel-based Learning Methods
SVMs<br>
the various techniques for adjusting the SVM class boundary
Active Learning Methods
SVM-based Active Learning
Simple Active Learning Heuristic (SALH) approach
Additional Methods
One-Class Learning
Mahalanobis-Taguchi System (MTS)
Multitask Learning Methodology
Ensemble Methods：boosting, bagging, cross-validation<br>
