Description: SGBDota (Stochastic Gradient Boosting with Double Targets) is a learning algorithm for the PCES (Positive Concept Expansion with Single snapshot) problem, which learns from training data as well as user provided preference.
Reference: Y. Yu and Z.-H. Zhou. A framework for modeling positive class expansion with single snapshot. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.429-440.
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 hole WEKA environment must be available. This package is developed with WEKA 3.4. 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. Yang Yu (yuy@lamda.nju.edu.cn). There are some javadoc files roughly explaining the codes, and a example usage file. But for any problem concerning the code, please feel free to contact Mr. Yu.
Download: [code&data] (222KB)