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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6509) MDLP discretizer
Date Tue, 24 Mar 2015 18:24:55 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6509?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14378323#comment-14378323
] 

Apache Spark commented on SPARK-6509:
-------------------------------------

User 'sramirez' has created a pull request for this issue:
https://github.com/apache/spark/pull/5170

> MDLP discretizer
> ----------------
>
>                 Key: SPARK-6509
>                 URL: https://issues.apache.org/jira/browse/SPARK-6509
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Sergio Ramírez-Gallego
>
> Minimum Description Lenght Discretizer
> This method implements Fayyad's discretizer [1] based on Minimum Description Length Principle
(MDLP) in order to treat non discrete datasets from a distributed perspective. We have developed
a distributed version from the original one performing some important changes.
> -- Improvements on discretizer:
>     Support for sparse data.
>     Multi-attribute processing. The whole process is carried out in a single step when
the number of boundary points per attribute fits well in one partition (<= 100K boundary
points per attribute).
>     Support for attributes with a huge number of boundary points (> 100K boundary
points per attribute). Rare situation.
> This software has been proved with two large real-world datasets such as:
>     A dataset selected for the GECCO-2014 in Vancouver, July 13th, 2014 competition,
which comes from the Protein Structure Prediction field (http://cruncher.ncl.ac.uk/bdcomp/).
The dataset has 32 million instances, 631 attributes, 2 classes, 98% of negative examples
and occupies, when uncompressed, about 56GB of disk space.
>     Epsilon dataset: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#epsilon.
400K instances and 2K attributes
> We have demonstrated that our method performs 300 times faster than the sequential version
for the first dataset, and also improves the accuracy for Naive Bayes.
> References
> [1] Fayyad, U., & Irani, K. (1993).
> "Multi-interval discretization of continuous-valued attributes for classification learning."



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