Show simple item record Shen, Qiang Jensen, Richard 2008-02-21T09:58:04Z 2008-02-21T09:58:04Z 2008-04-01
dc.identifier.citation Shen , Q & Jensen , R 2008 , ' Approximation-based feature selection and application for algae population estimation ' Applied Intelligence , vol 28 , no. 2 , pp. 167-181 . DOI: 10.1007/s10489-007-0058-y en
dc.identifier.issn 0924-669X
dc.identifier.other PURE: 75485
dc.identifier.other PURE UUID: dea049d1-742c-4643-ab5a-9b2f0578d318
dc.identifier.other dspace: 2160/504
dc.description Q. Shen and R. Jensen, 'Approximation-based feature selection and application for algae population estimation,' Applied Intelligence, vol. 28, no. 2, pp. 167-181, 2008. Sponsorship: EPSRC RONO: EP/E058388/1 en
dc.description.abstract This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valued nature of the domain features, unlike many existing approaches. This is accomplished through the use of fuzzy-rough approximations. The paper demonstrates the effectiveness of this research by proposing an estimator of algae populations, a system that approximates, given certain water characteristics, the size of algae populations. This estimator significantly reduces computer time and space requirements, decreases the cost of obtaining measurements and increases runtime efficiency, making itself more viable economically. By retaining only information required for the estimation task, the system offers higher accuracy than conventional estimators. Finally, the system does not alter the domain semantics, making any distilled knowledge human-readable. The paper describes the problem domain, architecture and operation of the system, and provides and discusses detailed experimentation. The results show that algae estimators using a fuzzy-rough feature selection step produce more accurate predictions of algae populations in general. en
dc.format.extent 15 en
dc.language.iso eng
dc.relation.ispartof Applied Intelligence en
dc.rights en
dc.subject Feature evaluation and selection en
dc.subject Data-driven knowledge acquisition en
dc.subject Classification en
dc.subject Fuzzy-rough sets en
dc.subject Algae population estimation en
dc.title Approximation-based feature selection and application for algae population estimation en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en

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