You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Gang Bai (JIRA)" <ji...@apache.org> on 2014/07/08 09:49:35 UTC
[jira] [Updated] (SPARK-2401) AdaBoost.MH, a multi-class
multi-label classifier
[ https://issues.apache.org/jira/browse/SPARK-2401?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Gang Bai updated SPARK-2401:
----------------------------
Description:
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
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.2#6252)