Download RedRF.java (Red-RF)

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Red-RF Overview

Reduced Random Forest for big data

Red(uced)-RF, a new type of Random Forests that adopts dynamic data reduction and weighted upvoting techniques. Red-RF is favorably applicable to big data: it demonstrates an accurate and efficient performance while achieving a considerable data reduction w.r.t. dataset size.

Manuscripts available on IEEE Xplore:

H. Mohsen, H. Kurban, K. Zimmer, M. Jenne and M. Dalkilic. Red-RF: Reduced Random Forests using priority voting & dynamic data reduction. In IEEE BigData Congress'2015.

H. Mohsen, H. Kurban, M. Jenne and M. Dalkilic (2014). A New Set of Random Forests with Varying Dynamic Data Reduction and Voting Techniques. In IEEE DSAA'2014.

Code, README file, and a sample input file are available in Files/ directory above.

For inquiries, please contact us at hmohsen@imail,iu.edu (or @indiana.edu).

Additional Details for Red-RF

Registered

2015-05-01

Last Updated

2015-12-01

Maintainers

husseinmohsen

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Reduced Random Forest for big data

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