[SPARK-2401] AdaBoost.MH, a multi-class multi-label classifier

• Type: New Feature
• Status: Resolved
• Priority: Trivial
• Resolution: Duplicate
• Component/s: MLlib
• Assignee: Unassigned
• Reporter: Gang Bai
• Created: 08/Jul/14 07:03
• Updated: 16/Jan/16 13:40
• Resolved: 16/Jan/16 13:40

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.

Issue Links

• duplicates: SPARK-1546 Add AdaBoost algorithm to Spark MLlib
• is required by: SPARK-3703 Ensemble learning methods