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https://issues.apache.org/jira/browse/MADLIB-1168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16444595#comment-16444595
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ASF GitHub Bot commented on MADLIB-1168:
----------------------------------------
Github user jingyimei commented on a diff in the pull request:
https://github.com/apache/madlib/pull/265#discussion_r182845348
--- Diff: RELEASE_NOTES ---
@@ -9,6 +9,56 @@ commit history located at
https://github.com/apache/madlib/commits/master.
Current list of bugs and issues can be found at
https://issues.apache.org/jira/browse/MADLIB.
—-------------------------------------------------------------------------
+MADlib v1.14:
+
+Release Date: 2018-April-28
+
+New features:
+* New module - Balanced datasets: A sampling module to balance
classification
+ datasets by resampling using various techniques including
undersampling,
+ oversampling, uniform sampling or user-defined proportion sampling
+ (MADLIB-1168)
+* Mini-batch: Added a mini-batch optimizer for MLP and a preprocessor
function
--- End diff --
Other JIRAs related to this: MADLIB-1220, MADLIB-1224, MADLIB-1226,
MADLIB-1227
> 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
> Assignee: ssoni
> Priority: Major
> Fix For: v1.14
>
> 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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