A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
On multi-class cost-sensitive learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A learning method for the class imbalance problem with medical data sets
Computers in Biology and Medicine
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Hi-index | 0.00 |
This paper describes a new learning strategy on the problem of classification on overlapped and imbalanced training set. We devise an adaptive scheme for minority generating; with data cleaning of majority, new clusters are drawn to increasingly focus on the combination of new minority samples. Inspired by the essence of SVM, we extract the most informative SVs for training. An empirical study compares the performance of our strategy against traditional classification approaches on the benchmark data sets, and experimental results show that our learning strategy not only inherent data distribution, but also improve classification effectiveness and efficiency.