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Posted to issues@spark.apache.org by "Yanbo Liang (JIRA)" <ji...@apache.org> on 2016/10/19 10:48:58 UTC
[jira] [Updated] (SPARK-17645) Add feature selector methods based
on: False Discovery Rate (FDR) and Family Wise Error rate (FWE)
[ https://issues.apache.org/jira/browse/SPARK-17645?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yanbo Liang updated SPARK-17645:
--------------------------------
Shepherd: Yanbo Liang
Assignee: Peng Meng
> Add feature selector methods based on: False Discovery Rate (FDR) and Family Wise Error rate (FWE)
> --------------------------------------------------------------------------------------------------
>
> Key: SPARK-17645
> URL: https://issues.apache.org/jira/browse/SPARK-17645
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
> Reporter: Peng Meng
> Assignee: Peng Meng
> Priority: Minor
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> 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
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