[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alexey Zinoviev updated IGNITE-9461:
Reporter: Alexey Zinoviev (was: Oleg Ignatenko)
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Reporter: Alexey Zinoviev
>Assignee: Alexey Zinoviev
>Priority: Major
> Fix For: 2.9
>
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alexey Zinoviev updated IGNITE-9461:
Fix Version/s: (was: 2.9)
3.0
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Reporter: Alexey Zinoviev
>Assignee: Alexey Zinoviev
>Priority: Major
> Fix For: 3.0
>
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alexey Zinoviev updated IGNITE-9461:
Affects Version/s: (was: 2.6)
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Reporter: Oleg Ignatenko
>Assignee: Alexey Zinoviev
>Priority: Major
> Fix For: 2.9
>
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Maxim Muzafarov updated IGNITE-9461:
Fix Version/s: (was: 2.8)
2.9
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Affects Versions: 2.6
>Reporter: Oleg Ignatenko
>Priority: Major
> Fix For: 2.9
>
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yury Babak updated IGNITE-9461:
---
Fix Version/s: 2.8
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Affects Versions: 2.6
>Reporter: Oleg Ignatenko
>Priority: Major
> Fix For: 2.8
>
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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[jira] [Updated] (IGNITE-9461) Implement random subspace method and provide an option to combine it with bagging
[
https://issues.apache.org/jira/browse/IGNITE-9461?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Oleg Ignatenko updated IGNITE-9461:
---
Ignite Flags: (was: Docs Required)
> Implement random subspace method and provide an option to combine it with
> bagging
> -
>
> Key: IGNITE-9461
> URL: https://issues.apache.org/jira/browse/IGNITE-9461
> Project: Ignite
> Issue Type: Task
> Components: ml
>Affects Versions: 2.6
>Reporter: Oleg Ignatenko
>Priority: Major
>
> Implement random subspace method (aka attribute bagging or feature bagging)
> to give ML API users more options to address overfitting. Also provide an
> option to combine this method with bagging.
> References:
> * [Wikipedia article|https://en.wikipedia.org/wiki/Random_subspace_method]
> {quote}Informally, this causes individual learners to not over-focus on
> features that appear highly predictive/descriptive in the training set, but
> fail to be as predictive for points outside that set. For this reason, random
> subspaces are an attractive choice for problems where the number of features
> is much larger than the number of training points, such as learning from fMRI
> data or gene expression data...{quote}
> * [Combining Bagging and Random Subspaces to Create Better
> Ensembles|https://pdfs.semanticscholar.org/d38f/979ad85d59fc93058279010efc73a24a712c.pdf]
> * [Bagging and the Random Subspace Method for Redundant Feature
> Spaces|https://link.springer.com/chapter/10.1007/3-540-48219-9_1]
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