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dc.contributor.authorBacardit, Jaume-
dc.contributor.authorBurke, Edmund-
dc.contributor.authorKrasnogor, Natalio-
dc.description.abstractEvolutionary learning techniques are comparable in accuracy with other learning methods such as Bayesian Learning, SVM, etc. These techniques often produce more interpretable knowledge than, e.g. SVM; however, efficiency is a significant drawback. This paper presents a new representation motivated by our observations that Bioinformatics and Systems Biology often give rise to very large-scale datasets that are noisy, ambiguous and usually described by a large number of attributes. The crucial observation is that, in the most successful rules obtained for such datasets, only a few key attributes (from the large number of available ones) are expressed in a rule, hence automatically discovering these few key attributes and only keeping track of them contributes to a substantial speed up by avoiding useless match operations with irrelevant attributes. Thus, in effective terms this procedure is performing a fine-grained feature selection at a rule-wise level, as the key attributes may be different for each learned rule. The representation we propose has been tested within the BioHEL machine learning system, and the experiments performed show that not only the representation has competent learning performance, but that it also manages to reduce considerably the system run-time. That is, the proposed representation is up to 2-3 times faster than state-of-the-art evolutionary learning representations designed specifically for efficiency purposes.en_UK
dc.relationBacardit J, Burke E & Krasnogor N (2009) Improving the scalability of rule-based evolutionary learning, Memetic Computing, 1 (1), pp. 55-67.-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectEvolutionary algorithmsen_UK
dc.subjectEvolutionary learningen_UK
dc.subjectLearning classifier systemsen_UK
dc.subjectRule inductionen_UK
dc.subjectProtein structure predictionen_UK
dc.titleImproving the scalability of rule-based evolutionary learningen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.citation.jtitleMemetic Computing-
dc.type.statusPublisher version (final published refereed version)-
dc.contributor.affiliationUniversity of Nottingham-
dc.contributor.affiliationComputing Science and Mathematics-
dc.contributor.affiliationUniversity of Nottingham-
Appears in Collections:Computing Science and Mathematics Journal Articles

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