淡江大學機構典藏:Item 987654321/58496
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62797/95867 (66%)
造訪人次 : 3750477      線上人數 : 452
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/58496


    題名: Feature Selection via Correlation Coefficient Clustering
    作者: Hsu, Hui-Huang;Hsieh, Cheng-Wei
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Feature Selection;Clustering;Correlation Coefficient;Support Vector Machines (SVMs);Machine Learning;Classification
    日期: 2010-12
    上傳時間: 2011-10-01 12:00:52 (UTC+8)
    出版者: Oulu: Academy Publisher
    摘要: Feature selection is a fundamental problem in machine learning and data mining. How to choose the most problem-related features from a set of collected features is essential. In this paper, a novel method using correlation coefficient clustering in removing similar/redundant features is proposed. The collected features are grouped into clusters by measuring their correlation coefficient values. The most class-dependent feature in each cluster is retained while others in the same cluster are removed. Thus, the most class-related and mutually unrelated features are identified. The proposed method was applied to two datasets: the disordered protein dataset and the Arrhythmia (ARR) dataset. The experimental results show that the method is superior to other feature selection methods in speed and/or accuracy. Detail discussions are given in the paper.
    關聯: Journal of Software 5(12), pp.1371-1377
    DOI: 10.4304/jsw.5.12.1371-1377
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    1796-217X_5(12)p1371-1377.pdf449KbAdobe PDF286檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋