Combining similarity in time and space for training set formation under concept drift

I. Zliobaite

    Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

    42 Citaten (Scopus)
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    Samenvatting

    Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications. Keywords: Concept drift, gradual drift, online learning, instance selection
    Originele taal-2Engels
    Pagina's (van-tot)589-611
    TijdschriftIntelligent Data Analysis
    Volume15
    Nummer van het tijdschrift4
    DOI's
    StatusGepubliceerd - 2011

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