Online Boosting Algorithms for Multi-label Ranking release_rmsmw3k3vvd2xlnffqk4wfphva

by Young Hun Jung, Ambuj Tewari

Released as a article .

2017  

Abstract

We consider the multi-label ranking approach to multi-label learning. Boosting is a natural method for multi-label ranking as it aggregates weak predictions through majority votes, which can be directly used as scores to produce a ranking of the labels. We design online boosting algorithms with provable loss bounds for multi-label ranking. We show that our first algorithm is optimal in terms of the number of learners required to attain a desired accuracy, but it requires knowledge of the edge of the weak learners. We also design an adaptive algorithm that does not require this knowledge and is hence more practical. Experimental results on real data sets demonstrate that our algorithms are at least as good as existing batch boosting algorithms.
In text/plain format

Archived Files and Locations

application/pdf  271.9 kB
file_ln62mg26azddxdhlu4eewvmor4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2017-10-23
Version   v1
Language   en ?
arXiv  1710.08079v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: d9e42db8-d5b2-432e-b09f-603eac859f25
API URL: JSON