Improving the classification accuracy in chemistry via boosting technique

Ping He, Cheng Jian Xu, Yi Zeng Liang, Kai Tai Fang*

*Bijbehorende auteur voor dit werk

    OnderzoeksoutputAcademicpeer review

    25 Citaten (Scopus)

    Samenvatting

    One of the main tasks of chemometrics is to classify chemical objects to one of several distinct predefined categories. There are many classification methods in data mining, one of which is the boosting technique that can improve predicate performance of a given classifier and it is one of the most powerful methods in classification methodology. In this paper, we apply boosting neural network (NN) and boosting tree in classification for chemical data. Experimental results show that boosting can significantly improve the prediction performance of any single classification method. Two techniques to interpret the model are also introduced in order to help us better understand the experimental results.

    Originele taal-2English
    Pagina's (van-tot)39-46
    Aantal pagina's8
    TijdschriftChemometrics and Intelligent Laboratory Systems
    Volume70
    Nummer van het tijdschrift1
    DOI's
    StatusPublished - 28-jan-2004

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