臺灣博碩士論文加值系統

在資料探勘技術中，分類演算法對於類別分佈平衡的資料集合上，其運用通常具有不錯的分類效能。然而在實務運用上，資料集合中的類別分布卻經常存在不平衡或不對稱性的情形，使得分類演算法所建構出之分類模式通常無法針對數量極少的目標資料進行正確之類別分類。
資料集合不平衡而導致分類錯誤，主要原因是少數類別數量太少及少數類別資料相近之多數類別資料，而造成分類模式分類錯誤。本研究應用上述論點並以多抽少數合成技術(Synthetic Minority Over-sampling Technique, SMOTE)為基礎，提出二個改善資料集合不平衡問題之新抽樣方法。本研究中採用捕捉率、準確率和F-measure與G-mean作為分類模式評估準則，並設定不同K個最近鄰居(K-NN)之K值，來對由UCI蒐集七個具高度不平衡資料集合進行實驗與分析。由實驗結果說明，本研究所提出之新抽樣演算法，在K<=7時，與參與實驗之其他抽樣演算法相比，確實能有效改善不平衡資料集中對少數類別資料之分類效能。

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