TriTrain is a semi-supervised algorithm, which iteratively refines each of the three component classifiers generated from the original labeled example set with the unlabeled examples based on the predictions the other classifiers agree on, and finally combines their prediction via majority voting.
Z.-H. Zhou and M. Li. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering. 2005, vol.17, no.11, pp.1529-1541.
This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (email@example.com ).
To use this package, the whole WEKA nvironment (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.
Both the input and output formats are the same as those used by WEKA.
This package was developed by Mr. Ming Li (firstname.lastname@example.org ). There is a ReadMe file roughly explaining the codes. For any problem concerning the code, please feel free to contact with Mr. Li.