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    Title: fMRI資料架構分析為主之分類研究
    A Geometry analysis - based classification study of fMRI patterns
    Authors: 章珅鎝
    Contributors: 周珮婷
    章珅鎝
    Keywords: fMRI
    DCG tree
    機器學習
    雙層距離
    Date: 2015
    Issue Date: 2015-07-13 11:06:30 (UTC+8)
    Abstract: 此篇論文研究哪種幾何架構較適合fMRI資料,我們使用DCG tree做分析,使用的資料為POP課題的紅與綠實驗數據,此資料的表現形式由Beta-series相關係數矩陣所呈現。在分析幾何形式時為了考慮變數分組之情形,使用了雙層距離的方法計算了個體間的距離。為避免太多變數導致有多餘的雜訊,使用了獨立雙樣本t檢定、主成份分析、個別區域之預測結果篩選出部分變數。我們使用交叉驗證的方式去算出我們的準確率,由DCG tree得到的分群結果,再使用cos⁡θ值去預測測試集的分類,為了使結果更好,我們提高DCG tree中的門檻值與將資料標準化。為了確認DCG tree較適合拿來做這類型研究,也使用SVM、LDA、KNN、K-means和HC tree這些演算法來與其做比較。最後得出使用歐幾里德雙層距離與t檢定篩選變數並提高門檻值能有最好的分類結果,且與其它方法比較後,也得出確實DCG tree有較精確的分類預測。
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    Description: 碩士
    國立政治大學
    統計研究所
    102354023
    103
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0102354023
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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