分類的工具當中，簡易貝氏分類器具備運作簡單而且快速的優勢，並且有著良好的分類正確率，因此被人們經常使用。簡易貝氏分類器在運作時，屬性是離散型才可發揮其快速的優點，因此遇到連續型的屬性一般是先經過離散化才可輸入，而混合型離散化藉由找到各連續型屬性較佳的離散型態，相較於屬性全採同一種離散化方法，更能提升分類正確率。特徵選取對於分類同樣十分重要，若能適當地採用便可大幅提升運算效率，挑選式簡易貝氏特徵選取方法是簡易貝氏分類器中最常採用的特徵選取機制，因為其運作原理簡易，挑選之後的結果也確實可以提升分類正確率。在過往的研究當中，將離散化與特徵選取結合的方法已屬少數，由於混合型離散化提出的時間距今仍近，因此結合混合型離散化與特徵選取並未有研究探討。本研究的目的便是嘗試結合混合型離散化與特徵選取，觀察將這二個機制結合後對於分類正確率是否有顯著的提升。本研究提出了三個方法，方法一為特徵選取完之後再行混合型離散化，執行速度快。方法二與方法三大體而言均為先做混合型離散化再做特徵選取，而基於運算效率與分類正確率的考量，兩個方法在設計上有所不同。方法二在混合型離散化時並未將所有的屬性可能全部納入；相對的，方法三付出了較高的運算複雜度，以納入所有可能的屬性組合。經過實驗證明，相較於資料檔的屬性均採用同一種離散方法離散化後再行簡易貝氏特徵選取，本研究提出的三個方法在分類正確率上都有較好的表現，說明了混合型離散化與簡易貝氏特徵選取的結合是有效益的，其中又以方法三的分類表現最佳。

Naïve Bayesian classifier is widely used for classification problems, because of its computational efficiency and competitive accuracy. Discretization is one of the major approaches for processing continuous attributes for naïve Bayesian classifier. Hybrid discretization sets the method for discretizing each continuous attribute individually. A previous study found that hybrid discretization is a better approach to improve the performance of the naïve Bayesian classifier than unified discretization. Selective naïve Bayes, abbreviated as SNB, is an important feature selection method for naïve Bayesian classifiers. It improves the efficiency and the accuracy by reducing redundant and irrelevant attributes. The object of this study is to develop methods composed of hybrid discretization and feature selection, and three methods for this purposed are proposed. Method one that is the most efficient executes hybrid discretization after feature selection. Methods two and three generally perform hybrid discretization first followed by feature selection. Method two transforms continuous attributes without considering discrete attributes, while method three determines the best discretization methods for each continuous attribute by searching all possibilities. The experimental results shows that in general, the three methods with hybrid discretization and feature selection all have better performance than the method with unified discretization and feature selection, and method three is the best.

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