適用於分類變數資料的二元不平衡資料自動分類系統

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/114438

Title:	適用於分類變數資料的二元不平衡資料自動分類系統
Other Titles:	Automatic binary classification system for imbalanced data with categorical explanatory variables
Authors:	葉丞峻;Yeh, Cheng-Chun
Contributors:	淡江大學統計學系碩士班陳景祥
Keywords:	classifier;data complexity;data mining;imbalanced data;分類技術;資料探勘;資料複雜度;類別不平衡
Date:	2017
Issue Date:	2018-08-03 14:52:36 (UTC+8)
Abstract:	隨著科技的進步，許多產業都應用自動化的作業模式，使得現今人類的生活更便利也更有效率。若我們能將自動化的概念導入資料分析的流程中，便能使資料分析者在作業上的負擔降低。本研究參考了資料複雜度指標對常見分類技術的影響，針對二元分類的類別不平衡資料，使用三種不同的重抽樣方法對資料進行類別的平衡，期望能夠建立一個有效的類別不平衡資料自動二元分類系統。研究結果顯示，本文提出的方法能夠有效的選出最好的分類技術。整體而言，羅吉斯迴歸在二元分類不平衡問題有較好的表現。 As technology advances, automated operations are used by many industries, it makes human life much easier and more efficient. Automated operations will reduce the burden on the data analyst if the concept of automation can be imported into the data analysis. In this study, influences of data complex indices on common classifier are evaluated and three different re-sampling methods are used for binray imbalanced data. The results show that our proposed procedure can effectively select the best classifier. For binary classification of imbalanced data, the Logistic regression has a better performance.
Appears in Collections:	[Graduate Institute & Department of Statistics] Thesis

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