Source code, demo, and lecture notes


This 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 at he@ele.uri.edu.


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:

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:

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.