Claudio Reggiani created MAHOUT-1152:
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Summary: mRMR feature selection algorithm
Key: MAHOUT-1152
URL: https://issues.apache.org/jira/browse/MAHOUT-1152
Project: Mahout
Issue Type: Improvement
Components: Integration
Affects Versions: 0.7
Reporter: Claudio Reggiani
Priority: Minor
Fix For: 0.8
Proposal Title: mRMR Feature Selection Algorithm on Map-Reduce.
Student Name: Claudio Reggiani
Student E-mail: [email protected]
Proposal Abstract:
The mRMR algorithm, described in [1], is a feature selection algorithm that
leverages mutual information evaluation to select features. At each iteration,
mRMR selects a new feature based on both how much it's strongly correlated to
the target output and how much it's less correlated to the features already
selected. The correlation is measured by means of mutual information. The
project proposes to provide the mRMR algorithm in MapReduce programming
framework.
Additional information:
1. *The code is already available* with some tests, because I'm working on my
master thesis an initial milestone of my research was to implement mRMR
algorithm in MapReduce.
2. I'm figuring out if it's possible for me to apply at Google Summer of Code
2013.
References:
[1] Hanchuan Peng, Fuhui Long, and Chris Ding
IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 27, No. 8, pp.1226-1238, 2005.
Link: http://penglab.janelia.org/papersall/docpdf/2005_TPAMI_FeaSel.pdf
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