分類一直是資料探勘中相當重要的一部分，透過資料所提供的資訊有助於人們去預測未來可能發生之事件，因此不同的分類器都有其優勢去處理不同特性之分類問題；其中，簡易貝氏分類器更是常用的分類器之一，其具有效率快且分類結果穩定等特性，因而在實務中也被廣泛使用。在過去相關於離散化之文獻中，主要研究目的在於如何發展出更適合簡易貝氏分類器的離散化方法以運用於同一個資料檔中之所有屬性；相對於此，本研究則是考量到不同屬性有其不同之特性，因此若使用同一種離散化方法去處理資料檔中之所有連續型屬性是不合理的，更正確的來講，為了使簡易貝氏分類器發揮更大的效益，應考量連續型屬性之特性以調整適當的離散化方法作搭配。首先，本研究挑選了文獻中能使簡易貝氏分類器表現較佳的幾個離散化技術作為基礎，並發展屬性採用個別離散化之調整機制，以期望能夠增加簡易貝氏分類器之績效。而在實證部分，則從UCI站上挑選到15個資料檔作測試，且測試結果皆呈現一致性的趨勢，即屬性採用個別離散化後所得到之分類正確率確實會比所有屬性採用相同之離散化方法時還要來得高，這樣的趨勢也顯示了當屬性採用個別離散化時對於簡易貝氏分類器是有幫助的。最後，為了探討分類績效提升的原因，本研究亦分析個別離散化前後對於資料檔之平均屬性相關性的影響，並推測分類正確率上升之因素在於屬性間相關性的降低。

Classification is one of the most important topics in data mining. It collects information to predict the class of a new instance. Naïve Bayesian classifiers are a widely used classification tool because of its computational efficiency. However, the naïve Bayesian classifier is not designed to process data with numerical attributes. Researchers therefore focus on developing discretization methods to process numerical attributes for naïve Bayesian classifiers. Since every attribute is unique, it is not totally reasonable to apply the same discretization technique on all numeric attributes in a data set. In this study, numeric attributes in a data set are allowed to be discretized by different methods to improve the performance of the naïve Bayesian classifier. An attribute discretization setting algorithm is proposed to find the appropriate discretization method for each numerical attribute. The experimental results on 15 data sets form UCI data repository demonstrate that our attribute discretization setting algorithm can generally achieve a higher prediction accuracy than the approach in which all numeric attributes are processed by the same discretization method. The major reason could be that the correlations among numeric attributes are reduced when they are processed by different discretization methods.

中文

黃偉碩 (2005)，「利用貝氏分類與因子分析法於半導體製程錯誤偵測與診斷」，中華大學/科技管理研究所碩士論文。
張良豪 (2009)，利用貝氏屬性挑選法與先驗分配提升簡易貝氏分類器之效能，國立成功大學工業管理研究所碩士班碩士論文。

英文

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