Reverse Multi-Label Learning
Petterson, James; Caetano, Tiberio
Description
Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classification admit...[Show more]
dc.contributor.author | Petterson, James | |
---|---|---|
dc.contributor.author | Caetano, Tiberio | |
dc.coverage.spatial | Sydney Australia | |
dc.date.accessioned | 2015-12-07T22:53:13Z | |
dc.date.created | November 22-25 2010 | |
dc.identifier.isbn | 9783642175336 | |
dc.identifier.uri | http://hdl.handle.net/1885/27764 | |
dc.description.abstract | Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classification admit relaxations that can be efficiently optimised. We optimise these relaxations with standard algorithms and compare our results with several stateof-the-art methods, showing excellent performance. | |
dc.publisher | Springer | |
dc.relation.ispartofseries | International Conference on Neural Information Processing (ICONIP 2010) | |
dc.source | Proceedings of the International Conference on Neural Information Processing (ICONIP 2010) | |
dc.subject | Keywords: Document Classification; Excellent performance; Gene function prediction; Image annotation; Multi-label; Multiple labels; Problem-based; Quality measures; Standard algorithms; State-of-the-art methods; Forecasting; Genes; Information retrieval systems | |
dc.title | Reverse Multi-Label Learning | |
dc.type | Conference paper | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
dc.date.issued | 2010 | |
local.identifier.absfor | 080109 - Pattern Recognition and Data Mining | |
local.identifier.ariespublication | u4963866xPUB53 | |
local.type.status | Published Version | |
local.contributor.affiliation | Petterson, James, College of Engineering and Computer Science, ANU | |
local.contributor.affiliation | Caetano, Tiberio, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 11 | |
local.identifier.absseo | 890299 - Computer Software and Services not elsewhere classified | |
dc.date.updated | 2016-02-24T11:30:50Z | |
local.identifier.scopusID | 2-s2.0-84860642004 | |
Collections | ANU Research Publications |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Petterson_Reverse_Multi-Label_Le_2010.pdf | 227.46 kB | Adobe PDF | Request a copy |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator