Multi-label lazy associative classification

Adriano Veloso, Wagner Meira, Marcos Gonçalves, Mohammed Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Citations (Scopus)

Abstract

Most current work on classification has been focused on learning from a set of instances that are associated with a single label (i.e., single-label classification). However, many applications, such as gene functional prediction and text categorization, may allow the instances to be associated with multiple labels simultaneously. Multi-label classification is a generalization of single-label classification, and its generality makes it much more difficult to solve. Despite its importance, research on multi-label classification is still lacking. Common approaches simply learn independent binary classifiers for each label, and do not exploit dependencies among labels. Also, several small disjuncts may appear due to the possibly large number of label combinations, and neglecting these small disjuncts may degrade classification accuracy. In this paper we propose a multi-label lazy associative classifier, which progressively exploits dependencies among labels. Further, since in our lazy strategy the classification model is induced on an instance-based fashion, the proposed approach can provide a better coverage of small disjuncts. Gains of up to 24% are observed when the proposed approach is compared against the state-of-the-art multi-label classifiers.

Original languageEnglish
Title of host publicationKnowledge Discovery in Database
Subtitle of host publicationPKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
Pages605-612
Number of pages8
Publication statusPublished - 1 Dec 2007
Event11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland
Duration: 17 Sep 200721 Sep 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4702 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
CountryPoland
CityWarsaw
Period17/9/0721/9/07

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Veloso, A., Meira, W., Gonçalves, M., & Zaki, M. (2007). Multi-label lazy associative classification. In Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 605-612). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI).