Improving the classification accuracy in chemistry via boosting technique

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

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    29 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)39-46
    Number of pages8
    JournalChemometrics and Intelligent Laboratory Systems
    Issue number1
    Publication statusPublished - 28-Jan-2004


    • Boosting
    • Chemometrics
    • Classification
    • Decision tree
    • Neural network

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