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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/125517
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/125517


    Title: 基於支持向量機計算的相互熵之特徵選取
    A Feature Selection Study based on SVM and Mutual Entropy
    Authors: 游上葦
    Yu, Shang-Wei
    Contributors: 周珮婷
    游上葦
    Yu, Shang-Wei
    Keywords: 機器學習
    特徵選取
    維度縮減
    支持向量機
    支援向量機

    相互熵
    Machine learning
    Feature selection
    Dimension reduction
    Support Vector Machine
    SVM
    Entropy
    Shannon Entropy
    Mutual Entropy
    Date: 2019
    Issue Date: 2019-09-05 15:42:06 (UTC+8)
    Abstract: 特徵選取為機器學習領域中一重要部分,適當的選取特徵(變數),除了減少機器運算時間、人力與金錢外,也可以避免模型過度配適或是欠擬和的情況發生。雖然經過多年發展已有很多特徵選取的方法,但同一種模型,不一定適用所有資料情況,因此提出新方法希望在特徵選取上會有更多選擇。
      本文提出一新方法衡量變數之間的關係,使用資訊理論中熵的概念,結合分類器支持向量機,獲取變數間關係,並將變數分群,給予每群適當的相對應分數,以此篩選變數。本文採用半監督式學習,計算敏感度、特異度與準確度之平均及所使用變數數量,並以高斯混合模型搭配EM演算法利用KS檢定之檢定統計量定義資料重要變數,評估方法能否選取重要變數,本文一共使用2筆模擬資料與5筆真實資料,並將結果與各大方法比較,結果顯示在各資料集中皆有穩定表現,即使在變數少的情況下也能有不錯表現。
    Feature selection technique plays a significant role in machine learning. Selecting features (variables) adequately can not only reduce the expenditure, operating time in machine and the cost of labor but also prevent under fitting or overfitting. Although lots of feature selection methods have been developed for decades, it is impossible to apply a unique method to all types of data sets. In this study, we propose a new method to calculate the correlation between variables based on the Shannon entropy from information theory and SVM classifier. Variables are grouped into several clusters and selected by the new correlation measurement. Besides, we define the importance of variable by the test statistic of KS test using Gaussian mixed model and E-M algorithm for the propose of result assessment. The performance of proposed method on two simulated data and five real data are demonstrated and compared with other feature selection methods. The predicted results are stable through the proposed method with a reduced dataset.
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    Description: 碩士
    國立政治大學
    統計學系
    106354024
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106354024
    Data Type: thesis
    DOI: 10.6814/NCCU201900996
    Appears in Collections:[統計學系] 學位論文

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