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https://issues.apache.org/jira/browse/SPARK-1473?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ignacio Zendejas updated SPARK-1473:
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Description:
For classification tasks involving large feature spaces in the order of tens of
thousands or higher (e.g., text classification with n-grams, where n > 1), it
is often useful to rank and filter features that are irrelevant thereby
reducing the feature space by at least one or two orders of magnitude without
impacting performance on key evaluation metrics (accuracy/precision/recall).
A feature evaluation interface which is flexible needs to be designed and at
least two methods should be implemented with Information Gain being a priority
as it has been shown to be amongst the most reliable.
Special consideration should be taken in the design to account for wrapper
methods (see research papers below) which are more practical for lower
dimensional data.
Relevant research:
* Brown, G., Pocock, A., Zhao, M. J., & Luján, M. (2012). Conditional
likelihood maximisation: a unifying framework for information theoretic
feature selection.*The Journal of Machine Learning Research*, *13*, 27-66.
* Forman, George. "An extensive empirical study of feature selection metrics
for text classification." The Journal of machine learning research 3 (2003):
1289-1305.
was:
For classification tasks involving large feature spaces in the order of tens of
thousands (e.g., text classification with n-grams, where n > 1), it is often
useful to rank and filter features that are irrelevant reducing the feature
space by at least one or two orders of magnitude without impacting performance
on key evaluation metrics (accuracy/precision/recall).
A feature evaluation interface which is flexible needs to be designed and at
least two methods should be implemented with Information Gain being a priority
as it has been shown to be amongst the most reliable.
Special consideration should be taken in the design to account for wrapper
methods (see research papers below) which are more practical for lower
dimensional data.
Relevant research:
* Brown, G., Pocock, A., Zhao, M. J., & Luján, M. (2012). Conditional
likelihood maximisation: a unifying framework for information theoretic
feature selection.*The Journal of Machine Learning Research*, *13*, 27-66.
* Forman, George. "An extensive empirical study of feature selection metrics
for text classification." The Journal of machine learning research 3 (2003):
1289-1305.
> Feature selection for high dimensional datasets
> -----------------------------------------------
>
> Key: SPARK-1473
> URL: https://issues.apache.org/jira/browse/SPARK-1473
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Ignacio Zendejas
> Priority: Minor
> Labels: features
> Fix For: 1.1.0
>
>
> For classification tasks involving large feature spaces in the order of tens
> of thousands or higher (e.g., text classification with n-grams, where n > 1),
> it is often useful to rank and filter features that are irrelevant thereby
> reducing the feature space by at least one or two orders of magnitude without
> impacting performance on key evaluation metrics (accuracy/precision/recall).
> A feature evaluation interface which is flexible needs to be designed and at
> least two methods should be implemented with Information Gain being a
> priority as it has been shown to be amongst the most reliable.
> Special consideration should be taken in the design to account for wrapper
> methods (see research papers below) which are more practical for lower
> dimensional data.
> Relevant research:
> * Brown, G., Pocock, A., Zhao, M. J., & Luján, M. (2012). Conditional
> likelihood maximisation: a unifying framework for information theoretic
> feature selection.*The Journal of Machine Learning Research*, *13*, 27-66.
> * Forman, George. "An extensive empirical study of feature selection metrics
> for text classification." The Journal of machine learning research 3 (2003):
> 1289-1305.
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