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FASBIR

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 multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact .

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] (101Kb)
  Name Size
- FASBIR.rar 100.31 KB

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