臺灣博碩士論文加值系統

不平衡數據在真實世界的數據集中無處不在。在本文中，我們研究了二分類的非平衡數據分佈。在二分類不平衡數據分佈中，多數類實例的數量顯著大於少數類實例的數量。傳統的機器學習演算法以經驗風險係數最小化為目標，雖然總體的正確率很高，但是由於少數類的數量相對稀少，因此往往會犧牲少數群體的分類精度。然而，少數類常常是人們感興趣的。為了改善不平衡數據分類的問題，研究者提出了數據層方法，如過採樣（over-sampling）和欠採樣（under-sampling），以及演算法層方法，如集成學習、代價敏感學習和單類學習。基於這些方法，我們提出了一種混合的處理非平衡數據問題的方法，混合方法包括數據預處理、對數據層次聚類、數據再平衡、模型構建、分類器的集成五個步驟。

Unbalanced data are ubiquitous in real-world datasets. In this paper, we investigate unbalanced data distribution for binary classification, i.e., where the number of majority class instances is significantly greater than the number of minority class instances. It is assumed that traditional machine learning algorithms attempt to minimize empirical risk factors, and, as a result, the classification accuracy of the minority is often sacrificed. However, people are often interested in the minority. Various data-level methods, such as over- and under-sampling, and algorithm-level methods, such as ensemble, cost-sensitive, and one-class learning, have been proposed to improve classifier performance with an unbalanced data distribution. Based on such methods, we proposed a hybrid approach to deal with unbalanced data problem that comprises data preprocessing, clustering, data balancing, model building, and ensemble.

目錄
摘要 I
ABSTRACT II
目錄 III
誌 謝 V
表目錄 VI
圖目錄 VI
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3創新點 3
1.4 論文框架 4
第二章、文獻回顧 5
2.1數據層次的方法 5
2.1.1隨機採樣法 5
2.1.2 SMOTE過採樣 5
2.1.3 Borderline-SMOTE過採樣 7
2.1.4 SMOTETomek方法 8
2.1.5 其它的數據再平衡方法 9
2.2演算法層次的方法 9
2.2.1集成學習 9
2.2.2代價敏感學習 10
2.3 SVM（SUPPORT VECTOR MACHINE） 11
2.3.1硬間隔支持向量機 11
2.3.2軟間隔支持向量機 13
2.3.3非線性 14
第三章 研究方法 15
3.1演算法框架 15
3.2數據預處理技術 16
3.3 層次聚類(HIERARCHICAL CLUSTERING) 17
3.4數據的再平衡處理 19
3.5 模型構建 20
3.6 集成學習(ENSEMBLE LEARNING) 20
3.7 評價指標 21
第四章 研究結果與分析 23
4.1數據信息 23
4.2實驗結果與分析 24
第五章 結論 48
參考文獻 50

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