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CSNN

Description: This package contains 6 algorithms for training cost-sensitive neural networks. They are over-sampling, under-sampling, threshold-moving, SMOTE and two ensemble methods, i.e. hard-ensemble and soft-ensemble.

Reference: Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).

Requirement: To use this package, Matlab 6.0 above with the neural network toolbox is required.

ATTN2: This package was developed by Ms. Xu-Ying Liu (liuxy@lamda.nju.edu.cn). There is a ReadMe file roughly explaining the codes. For any problem concerning the code, please feel free to contact Ms. Liu.

Download: code (36.3Kb)
  Name Size
- CSNN.rar 36.34 KB

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