Text representation in multi-label classification: Two new input representations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Automatic text classification is the task of assigning unseen documents to a predefined set of classes. Text representation for classification purposes has been traditionally approached using a vector space model due to its simplicity and good performance. On the other hand, multi-label automatic text classification has been typically addressed either by transforming the problem under study to apply binary techniques or by adapting binary algorithms to work with multiple labels. In this paper we present two new representations for text documents based on label-dependent term-weighting for multi-label classification. We focus on modifying the input. Performance was tested with a well-known dataset and compared to alternative techniques. Experimental results based on Hamming loss analysis show an improvement against alternative approaches.

Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms - 10th International Conference, ICANNGA 2011, Proceedings
Pages61-70
Number of pages10
EditionPART 2
DOIs
StatePublished - 2011
Externally publishedYes
Event10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011 - Ljubljana, Slovenia
Duration: 14 Apr 201116 Apr 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6594 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011
CountrySlovenia
CityLjubljana
Period14/04/1116/04/11

Keywords

  • Multi-label text classification
  • problem transformation
  • text modelling

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