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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94215

    Title: 資料複雜度指標在資料探勘分類方法之重要性
    Other Titles: Importance of the data complexity indices on classification methods in data mining
    Authors: 王詩詠;Wang, Shih Yung
    Contributors: 淡江大學統計學系碩士班
    Keywords: 資料複雜度;資料探勘;因素分析;分類器;分類正確率;data complexity;data mining;Factor Analysis;classifiers;classification correct rate
    Date: 2013
    Issue Date: 2014-01-23 14:10:27 (UTC+8)
    Abstract: 資料探勘中的分類技術經常被使用於處理各種分類問題,如何從眾多的分類技術中選擇合適的方法進行分析研究即成為一個重要的課題。以往大多數的學者對於分類器性能的評估,通常著重於比較分類器的預測正確率或模型訓練的速度等等。然而,在實務上,不同的分類問題皆有其獨特的資料結構,因此可能影響著分類器的表現。本研究使用了十五個資料複雜度指標(data complexity index)以量化分類問題的資料特徵,並對於此十五個資料複雜度指標進行因素分析,探索指標之間的重複性、相關性,將選出的因素當成此十五種資料複雜度指標的綜合指標。
    Classification techniques in data mining are often used to deal with a variety of classification problems. Choosing suitable methods for analysis from many classification techniques becomes an important issue. For the performance evaluations of the classifiers, researchers used to compare them on several datasets in terms of classification accuracy or training time, and so on. In practice, however, different classification problems has their unique data complexities which might affect the accuracies of the classifiers. Therefore, we adopt fifteen data complexity indices to quantify the data characteristics and use correct classification rate to observe the influence of these indices on seven commonly used classification techniques. We also use factor analysis to explore the correlation among these indices. The results show that different data characteristics indeed have impacts on classification performance. According to our studies, for classification problems, researchers can calculate the data complexity indices or factor values suggested in this paper to estimate the classification difficulties, and also choose the most appropriate classification method on their study.
    Appears in Collections:[統計學系暨研究所] 學位論文

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