Source code, demo, and lecture notes
website includes the source code, demos, and lecture notes for the
major research methods developed by the CISA group. If you need the
source code associated with any of our other papers, please feel free to contact us
This link includes the "Imbalanced
Learning" lecture notes, suitable for both graduate and undergraduate
courses on Machine Learning, Data Mining, Pattern Recognition, Big
Data, etc. This lecture notes is associated with the following
comprehensive survey paper:
H. He and E. A. Garcia, "Learning from Imbalanced Data," IEEE Trans.
Knowledge and Data Engineering, vol. 21, issue 9, pp. 1263-1284, 2009
This link inlcudes the source code and demo for the ADASYN algorithm, associatd with the following paper:
- ADASYN (Adaptive Synthetic Sampling) approach for imbalacned learning. [Source code link]
H. He, Y. Bai, E. A. Garcia, and S. Li, "ADASYN: Adaptive Synthetic
Sampling Approach for Imbalanced Learning," in Proc. Int. Joint Conf.
Neural Networks (IJCNN'08), pp. 1322-1328, 2008.
This link inlcudes the source code and demo for the ENN algorithm, associatd with the following paper:
B. Tang and H. He, "ENN: Extended Nearest Neighbor Method for Pattern
Recognition," IEEE Computational Intelligence Magazine, vol.10, no.3, pp.52 - 60, Aug. 2015
This link inlcudes the source code and demo for the SSC algorithm, associatd with the following paper:
- SSC (signal strength-based combining) for multiple classifier combination. [Source code link]
H. He and Y. Cao, "SSC: A Classifier Combination Method Based on Signal Strength," IEEE Trans. Neural Networks and Learning Systems, vol. 23, issue 7, pp. 1100-1117, 2012.