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https://ah.lib.nccu.edu.tw/handle/140.119/124685| 題名: | 高維不平衡基因資料的變數選取 Feature selection for high-dimensional imbalanced microarray data |
作者: | 董承 Tung, Chen |
貢獻者: | 周珮婷 CHOU, PEI-TING 董承 Tung, Chen |
關鍵詞: | 不平衡資料 高維度資料 基因微陣列資料 雙分群方法 變數選取 Imbalanced data High-dimensional data Microarray data Biclustering algorithm Feature selection |
日期: | 2019 | 上傳時間: | 7-Aug-2019 | 摘要: | 不平衡資料在各個領域中是一種常見的資料型態，少數類別通常是主要研究的目標，例如：異常偵測、風險管控、醫療診斷等領域。基因微陣列資料是利用生物晶片提取基因表現情形將其數據化，並對其進行研究分析，而此資料之特色為樣本數少卻有非常高的維度。本研究基於以上兩者之問題，對高維不平衡之基因微陣列資料，以雙分群方法之概念做變數選取，並且與F-test method、Cho’s method以及使用全部變數做比較，研究結果顯示本研究方法與F-test method表現接近且優於Cho’s method和使用全部變數。 Imbalanced data is a common data type in different fields, for example, novelty detection, risk management, medical diagnosis and so on. In these data types, minority class is usually the main target to study. In this study, we focus on microarray data. Microarray data is obtained by using biochips to extract gene expression, and then analyze it. The characteristics of this data is that the sample size is small but with a very high dimension. Based on the problems above, this study selects features of high-dimensional imbalanced microarray data by the concept of biclustering algorithm, and compares it with the F-test method, the Cho`s method, and using all variables. The performance of proposed method is similar to the F-test method and superior to the Cho`s method and using all variables. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0106354014 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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