The researches on feature selection play a very important role in the area of classification. In this paper, we introduce a heuristic wrapper method: Classification Contribution-Ratio Based Selection (CCRS). Using RBF neural network as a classifier, we did our experiments on a data set of 74 features extracted from 517 Chinese folk songs which come from 10 regions. The results show that Root Mean Square, Spectral Flux and Linear Prediction Coefficient are very effective for the classification of 10 kinds of Chinese folk songs. It works better when the number of features is reduced from 74 to 30 and the classification accuracy is improved from 39.74% (using the total 74 features) to 43.208% (using 30 optimal features). At last, we give an illustration of validity of the algorithm, and a comparison with the Fisher Criterion Method which shows the efficiency of CCRS.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Feature Selection for Automatic Classification of Chinese Folk Songs


    Beteiligte:
    Xu, Jieping (Autor:in) / Wang, Peng (Autor:in) / Yan, Li (Autor:in)


    Erscheinungsdatum :

    2008-05-01


    Format / Umfang :

    380324 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A novel feature selection framework for automatic web page classification

    Alamelu Mangai, J. / Santhosh Kumar, V. / Appavu alias Balamurugan, S. | British Library Online Contents | 2012


    AUTOMATIC VEHICLE FEATURE SELECTION

    PRZYBYLSKI MATTHEW G / ZINSER CRAIG H / CREEHAN JAMES SCOTT | Europäisches Patentamt | 2024

    Freier Zugriff

    Algorithm composition of Chinese folk music based on swarm intelligence

    Zheng, Xiaomei / Wang, Lei / Li, Dongyang et al. | British Library Online Contents | 2017


    Feature Detection with Automatic Scale Selection

    Lindeberg, T. | British Library Online Contents | 1998


    FRONT FOLK

    TSUYAMA SHIZUO | Europäisches Patentamt | 2017

    Freier Zugriff