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dc.contributor.authorMutter, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorHolmes, Geoffrey
dc.coverage.spatialConference held at Melbourne, Australiaen_NZ
dc.identifier.citationMutter, S., Pfaringer, B. & Holmes, G. (2009). The positive effects of negative information: Extending one-class classification models in binary proteomic sequence classification. In R. Goebel, J. Siekmann & W. Wahlster (Eds.), Proceedings of AI 2009: Advances in Artificial Intelligence, Melbourne, Australia, December 1-4 2009. (pp. 260-269). Springer-Verlag Berlin Heidelberg.en_NZ
dc.description.abstractProfile 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.en_NZ
dc.sourceAI 2009en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectProfile Hidden Markov Modelsen_NZ
dc.subjectMachine learning
dc.titleThe positive effects of negative information: Extending one-class classification models in binary proteomic sequence classificationen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProc 22nd Australasian Joint Conference on Advances in Artificial Intelligenceen_NZ
pubs.volumeLNAI 5866en_NZ

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