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dc.contributor.advisorTerziyan, Vagan
dc.contributor.authorNikulin, Anton
dc.date.accessioned2018-03-28T14:18:19Z
dc.date.available2018-03-28T14:18:19Z
dc.date.issued2018
dc.identifier.otheroai:jykdok.linneanet.fi:1863670
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/57461
dc.description.abstractThe size of databases has been considerably growing over recent decades and Machine Learning algorithms are not ready to process such large volume of information. Being one of the most useful algorithms in Data Mining the Nearest neighbor classifier suffers from high storage requirements and slow response when working with large data sets. Prototype Selection methods help to alleviate this problem by choosing a subset of data with a smaller size. In this thesis, the overview of existing instance selection methods is provided together with the introduction of a new approach. The majority of current methods select a subset experimentally by checking whether certain point affects classification accuracy or not. The new approach, presented in this thesis, is based on analyzing data set instances and choosing prototypes based on discovered ignorance zones. The results obtained from the analysis show that the proposed method can effectively decrease the size of the data set while maintaining the same classification accuracy with the Nearest neighbor classifier. In addition, it allows removing noisy data making the decision boundaries smoother.en
dc.format.extent1 verkkoaineisto (67 sivua)
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsJulkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.fi
dc.rightsThis publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.en
dc.subject.otherPrototype selection
dc.subject.otherNearest neighbor
dc.subject.otherIgnorance zones
dc.subject.otherData reduction
dc.subject.otherClassification
dc.titleSmart prototype selection for machine learning based on ignorance zones analysis
dc.identifier.urnURN:NBN:fi:jyu-201803281873
dc.type.ontasotPro gradu -tutkielmafi
dc.type.ontasotMaster’s thesisen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.tiedekuntaFaculty of Information Technologyen
dc.contributor.laitosInformation Technologyen
dc.contributor.laitosInformaatioteknologia
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.date.updated2018-03-28T14:18:20Z
dc.rights.accesslevelopenAccessfi
dc.type.publicationmasterThesis
dc.contributor.oppiainekoodi602
dc.subject.ysoprototyypit
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
dc.type.okmG2


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