Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.

Reference:   Z.-H. Zhou and Y. Yu. Ensembling local learners through multi-modal perturbation. IEEE Transactions on systems, man, and cybernetics - part B, inpress.

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. But for any problem concerning the code, please feel free to contact Mr. Yu.

Download:   [code] (99.9Kb)