Description:  

CoForest is a semi-supervised algorithm, which exploits the power of ensemble learning and large amount of unlabeled data available to produce hypothesis with better performance.

Reference:

M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098.

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, the whole WEKA  environment (ver 3.4) must be available. Refer: I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA, 2000.

Data format:

Both the input and output formats are the same as those used by WEKA.

ATTN2:

This package was developed by Mr. Ming Li (lim@lamda.nju.edu.cn). This ReadMe file roughly explains the codes. For any problem concerning the code, please feel free to contact Mr. Li.