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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/119716
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/119716

    Title: 以一致性進行特徵選取
    Feature Selection Based On Kappa Statistics
    Authors: 沈妤芳
    Shen, Yu-Fang
    Contributors: 周珮婷

    Shen, Yu-Fang
    Keywords: 機器學習
    Machine learning
    Feature selection
    Date: 2018
    Issue Date: 2018-08-29 15:47:32 (UTC+8)
    Abstract: 本文針對特徵選取做主要研究對象,在機器學習中分類是很重要的一環,從過去到現在有許多方法和文獻採用特徵冗餘的方式消除彼此間相對不重要進而保留其他相關性差異大的變數,這裡將針對特徵配適後的模型做分類後,對於特徵之間的關係在這裡選用Kappa一致性為一指標,再透過分類結果相似的特徵組合起來作為特徵選取的方法,與其他如:Random Forest、ReliefF、mRMR和建構在Symmetric Uncertainty的特徵選取演算法下做比較,對於準確度和變數子集合數量發現都有不錯的效果。
    Feature selection plays an important role in supervised learning by eliminating irrelevant features and improving classification results. The current study proposed a feature selection method based on Kappa statistics to select consistent features. SVM was used as a single-variable classifier and Kappa statistics was computed from the fitted results as an indicator of relationship between features. The proposed method was compared with other methods such as, Random Forest, ReliefF, mRMR, and Symmetric Uncertainty based method. The results showed that the proposed method can effectively select important features and achieve stable prediction performance.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105354014
    Data Type: thesis
    DOI: 10.6814/THE.NCCU.STAT.016.2018.B03
    Appears in Collections:[統計學系] 學位論文

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