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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/63086


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    題名: Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Values
    作者: 徐國偉
    Hsu,Kuo-Wei
    貢獻者: 資科系
    關鍵詞: Bagging;missing values;multilayerperceptron;sequential minimal optimization
    日期: 2013-10
    上傳時間: 2013-12-27 18:06:28 (UTC+8)
    摘要: Bagging is commonly used to improve the performance of a classification algorithm by first using bootstrap sampling on the given data set to train a number of classifiers and then using the majority voting mechanism to aggregate their outputs. However, the improvement would be limited in the situation where the given data set contains missing values and the algorithm used to train the classifiers is sensitive to missing values. We propose an extension of bagging that considers not only the weights of the classifiers in the voting process but also the incompleteness of the bootstrapped data sets used to train the classifiers. The proposed extension assigns a weight to each of the classifiers according to its classification performance and adjusts the weight of each of the classifiers according to the ratio of missing values in the data set on which it is trained. In experiments, we use two classification algorithms, two measures for weight assignment, and two functions for weight adjustment. The results reveal the potential of the proposed extension of bagging for working with classification algorithms sensitive to missing values to perform classification on data sets having small numbers of instances but containing relatively large numbers of missing alues.
    關聯: International Journal of Information and Education Technology, 3(5),560-566
    資料類型: article
    DOI 連結: http://dx.doi.org/10.7763/IJIET.2013.V3.335
    DOI: 10.7763/IJIET.2013.V3.335
    顯示於類別:[資訊科學系] 期刊論文

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