臺灣博碩士論文加值系統

在這個科技高速發展的社會中，人工智慧、物聯網等名詞大家耳熟能詳，而架構出這社會主流的基層即為數據，因此在各式各樣不同型態的數據集合中，需要有不同或是特殊的方法用以應對分析。然而在進行數據分析時，若是遇到數據當中不同類別的樣本數量比例失衡，就會導致類別不平衡的學習問題。根據過往的分類學習演算法，對於高度類別不平衡資料的學習，常會將少數類別的資料分類錯誤，而此些少數類別資料相較於多數類別資料，代表重要的意義或巨大成本。因此，在類別比例差異懸殊的資料中提升少數類別的分類正確率已然成為重要的議題。虛擬少數類別過抽樣技術(synthetic minority oversampling technique, SMOTE)是常用於解決類別不平衡問題的方法之一，其方法是任取一個少數類別樣本做為種子樣本，並找出周遭同為少數類別樣本後任取其一作為選取樣本後，並在兩個少數類別樣本之間生成虛擬樣本。然而本研究則考慮多數類別樣本與少數類別樣本以及少數類別樣本和少數類別樣本之間的影響力，提出基於局部空間訊息量之SMOTE(Local Information Index SMOTE, LII-SMOTE)，當不平衡資料集經由本文所提出之方法生成虛擬樣本後，其少數類別樣本的評估指標相較其他SMOTE能有效提升。

A dataset is imbalanced if the classes are not approximately equally represented. Data mining on imbalanced datasets receives more and more attentions in recent years. The class imbalanced problem occurs when there’s just few number of sample in one classes comparing to other classes. The SMOTE : Synthetic Minority Over-Sampling Technique is an effective method to solve imbalanced learning problem. The way is to take one of the minority sample as the seed sample, and find the minority sample nearby as the selected sample. After finding seed sample and selected sample, we generate virtual sample between two minority samples. Therefore, in this paper we consider the influence between majority samples and the selected sample and the influence between minority samples and the selected sample. This study develops a new sample-generating procedure by local majority class information and local minority class information. Four datasets taken from UCI Machine Learning Repository in experiments. We compare the proposed method with SMOTE and other extension version including Borderline SMOTE1(B1-SMOTE), Safe-Level SMOTE(SL-SMOTE), Local-Neighborhood SMOTE(LN-SMOTE), and ADASYN. The result shows that the proposed method achieve better classifier performance for the minority class than other methods after examined the data sets with C4.5 decision trees.

摘要 i
英文摘要 ii
致謝 xiii
表目錄 xvi
圖目錄 xvii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 5
1.3 研究目的 7
1.4 研究架構 7
第二章 文獻回顧 9
2.1 不平衡資料及其評估指標 9
2.1.1 不平衡資料學習問題 9
2.1.2 不平衡資料之評估指標 11
2.2 不平衡資料之研究方法回顧 14
2.2.1 不平衡資料學習的主要方法 14
2.2.2 不平衡資料學習的最新方法 17
2.3 SMOTE及SMOTE之延伸方法 17
2.3.1 SMOTE 17
2.3.2 SMOTE演算法的延伸 19
2.4 群聚分析 25
第三章 研究方法 27
3.1本研究運用之符號及其解釋 27
3.2 樣本資料集之分群 28
3.3 局部空間訊息量 30
3.3.1 局部空間多數類別樣本訊息量 31
3.3.2 局部空間少數類別樣本訊息量 33
3.4 虛擬樣本之生成 36
3.5 流程步驟 37
第四章 實證研究 39
4.1 實驗環境 39
4.1.1 實驗方式 39
4.1.2 評估指標 40
4.1.3 實驗資料 42
4.1.4 分類模式建構軟體 43
4.1.4 距離閥(dc)敏感度分析 44
4.2 實驗結果 46
4.2.1 Haberman資料集及實驗結果 46
4.2.2 Pima資料集及實驗結果 51
4.2.3 Phoneme資料集及實驗結果 55
4.2.4 Satimage資料集及實驗結果 59
第五章 結論 63
5.1結論 63
5.2未來研究建議 64
參考文獻 65

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