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    Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
    (2021-09-01)
    Changpetch, Pannapa
    ;
    Pitpeng, Apasiri
    ;
    Hiriote, Sasiprapa
    ;
    Yuangyai, Chumpol
    In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the per-formance of the latter. A classification tree was used to discretize quantitative predictors into cate-gories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
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    Classification of high-resolution NMR spectra based on complex wavelet domain feature selection and kernel-induced random forest
    (2010-12-01)
    Fan, Guangzhe
    ;
    Wang, Zhou
    ;
    Kim, Seoung Bum
    ;
    Temiyasathit, Chivalai
    High-resolution nuclear magnetic resonance (NMR) spectra contain important biomarkers that have potentials for early diagnosis of disease and subsequent monitoring of its progression. Traditional features extraction and analysis methods have been carried out in the original frequency spectrum domain. In this study, we conduct feature selection based on a complex wavelet transform by making use of its energy shift-insensitive property in a multi-resolution signal decomposition. A false discovery rate based multiple testing procedure is employed to identify important metabolite features. Furthermore, a novel kernel-induced random forest algorithm is used for the classification of NMR spectra based on the selected features. Our experiments with real NMR spectra showed that the proposed method leads to significant reduction in misclassification rate. © Springer-Verlag Berlin Heidelberg 2010.