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https://issues.apache.org/jira/browse/MADLIB-1168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16295847#comment-16295847
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ASF GitHub Bot commented on MADLIB-1168:
----------------------------------------
GitHub user Swatisoni opened a pull request:
https://github.com/apache/madlib/pull/218
Balanced Datasets: Random undersampling with/without replacement
JIRA:MADLIB-1168
Additional Authors:
Orhan Kislal <[email protected]>
This commit implements random undersampling to create a dataset
with balanced classes.
Both with- and without-replacement methods are available.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/Swatisoni/madlib feature/balanced_sets
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/madlib/pull/218.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 #218
----
commit 010199cbd2d14f13eca76330d54fc6e29fb9ecee
Author: Swatisoni <[email protected]>
Date: 2017-12-18T23:30:41Z
Balanced Datasets: Random undersampling with/without replacement
JIRA:MADLIB-1168
Additional Authors:
Orhan Kislal <[email protected]>
This commit implements random undersampling to create a dataset
with balanced classes.
Both with- and without-replacement methods are available.
----
> Balance datasets
> ----------------
>
> Key: MADLIB-1168
> URL: https://issues.apache.org/jira/browse/MADLIB-1168
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Sampling
> Reporter: Frank McQuillan
> Fix For: v2.0
>
> Attachments: MADlib Balance Datasets Requirements.pdf,
> MADlib_Balance_Datasets_Requirements_v2.pdf
>
>
> From [1] here is the motivation behind balancing datasets:
> “Most classification algorithms will only perform optimally when the number
> of samples of each class is roughly the same. Highly skewed datasets, where
> the minority is heavily outnumbered by one or more classes, have proven to be
> a challenge while at the same time becoming more and more common.
> One way of addressing this issue is by re-sampling the dataset as to offset
> this imbalance with the hope of arriving at a more robust and fair decision
> boundary than you would otherwise.
> Re-sampling techniques can be divided in these categories:
> * Under-sampling the majority class(es).
> * Over-sampling the minority class.
> * Combining over- and under-sampling.
> * Create ensemble balanced sets.”
> There is an extensive literature on balancing datasets. The plan for MADlib
> in the initial phase is to offer basic functionality that can be extended in
> later phases based on feedback from users.
> Please see attached document for proposed scope of this story.
> References
> [1] imbalance-learn Python project
> http://contrib.scikit-learn.org/imbalanced-learn/stable/index.html
> https://github.com/scikit-learn-contrib/imbalanced-learn
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