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https://issues.apache.org/jira/browse/SPARK-2401?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Gang Bai updated SPARK-2401:
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Description:
Multi-class multi-label classifiers are very useful in web page profiling,
audience segmentation etc. The goal of a multi-class multi-label classifier is
to tag a sample data point with a subset of labels from a finite, pre-specified
set, e.g. tagging a visitor with a set of interests. Given a set of L labels, a
data point can be tagged with one of the 2^L possible subsets. The main
challenges in training a multi-class multi-label classifier are the
exponentially large label space.
This JIRA is created to track the effort of solving the training problem of
multi-class, multi-label classifiers by implementing AdaBoost.MH on Apache
Spark. It will not be an easy task. I will start from a basic DecisionStump
weak learner and a simple Hamming tree resembling DecisionStumps into a meta
weak learner, and the iterative boosting procedure. I will be reusing modules
of Alexander Ulanov's multi-class and multi-label metrics evaluation and Manish
Amde's decision tree/boosting/ensemble implementations.
was:
The goal of a multi-class multi-label classifier is to tag a sample data point
with a subset of labels from a finite, pre-specified set. Given a set of L
labels, a data point can be tagged with one of the 2^L possible subsets. The
main challenges in training a multi-class multi-label classifier are the
exponentially large label space.
Multi-class multi-label classifiers are very useful in
This JIRA is created to track the effort of solving the training problem of
multi-class, multi-label classifiers by implementing AdaBoost.MH on Apache
Spark. It will not be an easy task. I will start from a basic DecisionStump
weak learner and a simple Hamming tree resembling DecisionStumps into a meta
weak learner, and the iterative boosting procedure. I will be reusing modules
of Alexander Ulanov's multi-class and multi-label metrics evaluation and Manish
Amde's decision tree/boosting/ensemble implementations.
> AdaBoost.MH, a multi-class multi-label classifier
> -------------------------------------------------
>
> Key: SPARK-2401
> URL: https://issues.apache.org/jira/browse/SPARK-2401
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Gang Bai
>
> Multi-class multi-label classifiers are very useful in web page profiling,
> audience segmentation etc. The goal of a multi-class multi-label classifier
> is to tag a sample data point with a subset of labels from a finite,
> pre-specified set, e.g. tagging a visitor with a set of interests. Given a
> set of L labels, a data point can be tagged with one of the 2^L possible
> subsets. The main challenges in training a multi-class multi-label classifier
> are the exponentially large label space.
> This JIRA is created to track the effort of solving the training problem of
> multi-class, multi-label classifiers by implementing AdaBoost.MH on Apache
> Spark. It will not be an easy task. I will start from a basic DecisionStump
> weak learner and a simple Hamming tree resembling DecisionStumps into a meta
> weak learner, and the iterative boosting procedure. I will be reusing modules
> of Alexander Ulanov's multi-class and multi-label metrics evaluation and
> Manish Amde's decision tree/boosting/ensemble implementations.
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