The positive effects of negative information: Extending one-class classification models in binary proteomic sequence classification

Stefan Mutter, Bernhard Pfahringer, Geoffrey Holmes

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

1 Citation (Scopus)

Abstract

Profile Hidden Markov Models (PHMMs) have been widely used as models for Multiple Sequence Alignments. By their nature, they are generative one-class classifiers trained only on sequences belonging to the target class they represent. Nevertheless, they are often used to discriminate between classes. In this paper, we investigate the beneficial effects of information from non-target classes in discriminative tasks. Firstly, the traditional PHMM is extended to a new binary classifier. Secondly, we propose propositional representations of the original PHMM that capture information from target and non-target sequences and can be used with standard binary classifiers. Since PHMM training is time intensive, we investigate whether our approach allows the training of the PHMM to stop, before it is fully converged, without loss of predictive power.

Original languageEnglish
Title of host publicationAI 2009
Subtitle of host publicationAdvances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings
Pages260-269
Number of pages10
DOIs
Publication statusPublished or Issued - 1 Dec 2009
Event22nd Australasian Joint Conference on Artificial Intelligence, AI 2009 - Melbourne, VIC, Australia
Duration: 1 Dec 20091 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5866 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd Australasian Joint Conference on Artificial Intelligence, AI 2009
CountryAustralia
CityMelbourne, VIC
Period1/12/091/12/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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