GitHub user mpjlu opened a pull request:

    https://github.com/apache/spark/pull/15212

    [MLLIB][ML]add feature selector method based on: False Discovery Rate (FDR) 
and Family wise error rate (FWE)

    ## What changes were proposed in this pull request?
    Univariate feature selection works by selecting the best features based on 
univariate statistical tests. 
    FDR and FWE are a popular univariate statistical test for feature 
selection.  
    In 2005, the Benjamini and Hochberg paper on FDR was identified as one of 
the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg 
procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate. 
    In statistics, FWE is the probability of making one or more false 
discoveries, or type I errors, among all the hypotheses when performing 
multiple hypotheses tests.
    https://en.wikipedia.org/wiki/Family-wise_error_rate
    
    We add  FDR and FWE methods for ChiSqSelector in this PR, like it is 
implemented in scikit-learn. 
    
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
    
    ## How was this patch tested?
    
    ut will be added soon
    
    (Please explain how this patch was tested. E.g. unit tests, integration 
tests, manual tests)
    
    
    (If this patch involves UI changes, please attach a screenshot; otherwise, 
remove this)
    
    


You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/mpjlu/spark fdr_fwe

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/spark/pull/15212.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #15212
    
----
commit 2c071791b2c6fd7d388343ac95783c32ffdae529
Author: Peng, Meng <peng.m...@intel.com>
Date:   2016-09-23T07:27:19Z

    add feature selector method: FDR and FWE

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