由於應用常見的分類分析技術在類別數量分佈平均的資料集合上，即可建構出預測效能良好的分類模式。然而，在如信用卡詐欺偵測的實務運用上，資料集合內卻常存在著類別間數量分佈極不平均的非對稱性分配問題，因此以一般的分類分析技術所建構出的分類模式，常有嚴重的類別預測偏向問題，使得預測模式無法對數量稀少的目標資料做出正確的類別預測。

減少多數法、增加少數法及多專家分類器等處理策略是目前文獻上常用以解決資料集合的非對稱性分配問題的方法，但卻少有文獻比較這些處理策略間的效能差異。因此本研究收集了十組具有非對稱性分配問題的資料集合，分別先以減少多數法、增加少數法及多專家分類器等策略處理資料集合內的非對稱性，再利用常見的C4.5決策樹建構分類器，進而比較各種非對稱處理策略間的效能差異，藉以瞭解各種處理策略的特性與適用的情境。

本研究收集了十組具有非對稱性問題的資料集合，並利用十摺交互驗證法(10-fold cross-validation)的實證評估方法，以分類精確度、回應率及F1衡量等三種效標，比較不同處理策略的效能差異。實證結果顯示，多專家分類器處理策略在各種效標下皆能有效地提昇分類器對少數類別資料的分類效能；倘若實務應用著重於分類器回應率的效能表現，則利用增加少數法將較能有效地提昇分類器的分類效能；若實務應用著重於分類器精確度的表現，則建議直接以原資料集合建構分類器。

Existing classification analysis techniques (e.g., decision tree induction,) generally exhibit satisfactory classification effectiveness when dealing with data with non-skewed class distribution. However, real-world applications (e.g., churn prediction and fraud detection) often involve highly skewed data in decision outcomes. Such a highly skewed class distribution problem, if not properly addressed, would imperil the resulting learning effectiveness.

In this study, we empirically evaluate three different approaches, namely the under-sampling, the over-sampling and the multi-classifier committee approaches, for addressing classification with highly skewed class distribution. Due to its popularity, C4.5 is selected as the underlying classification analysis technique. Based on 10 highly skewed class distribution datasets, our empirical evaluations suggest that the multi-classifier committee generally outperformed the under-sampling and the over-sampling approaches, using the recall rate, precision rate and F1-measure as the evaluation criteria. Furthermore, for applications aiming at a high recall rate, use of the over-sampling approach will be suggested. On the other hand, if the precision rate is the primary concern, adoption of the classification model induced directly from original datasets would be recommended.

第一章 緒論1
第一節 研究背景1
第二節 研究動機與目的2
第三節 論文架構3
第二章 文獻探討4
第一節 分類分析技術4
一、決策樹4
二、倒傳遞類神經網路5
三、最近鄰居分類法7
第二節 非對稱性問題的處理策略8
一、減少多數法8
二、增加少數法10
三、多專家分類器11
第三章 實證資料集合13
第四章 實證評估33
第一節 減少多數法方法建立33
第二節 增加少數法方法建立34
第三節 評估程序與評估指標36
第四節 實證結果分析39
第五章 結論67
第一節 綜合結論與貢獻67
第二節 未來研究方向68
參考文獻69

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