根據以往的研究文獻得知，主流研究使用的集成演算法建構出的簡易貝氏集成模型，其分類預測效能上與單一模型的簡易貝氏分類器相較起來，並無出現顯著的提升狀況，而造成這種現象的原因，則是因為簡易貝氏分類器其演算法的學習機制，使得簡易貝氏分類器是一種相對穩定的分類方法，也是因為其相較穩定的因素，導致簡易貝氏分類器的訓練情況，往往都會受到原始資料集分布的限制，進而使建構的簡易貝氏集成模型的預測能力與單一模型的簡易貝氏分類器有著相同的分類水準。因此本研究秉持著讓簡易貝氏分類器不受限於原始資料集分布的框架下，使用隨機生成模型的研究方式，並透過過濾分類正確率的方式，找出有著適當分類水準的隨機簡易貝氏模型，建構出簡易貝氏集成模型。最後再根據本研究隨機產生簡易貝氏模型所建構出的簡易貝氏集成模型，去探討此集成模型研究方式，是否可以讓簡易貝氏分類器在建構同質型集成模型的情況下，相較於以往文獻主流的集成演算法所建構的簡易貝氏集成模型，有著分類正確率上的提升與改善。本研究提出隨機生成簡易貝氏模型建構出簡易貝氏集成模型的方式，在對於20個二類別的資料集測試中，總共有15個資料集的分類正確率是最優秀的；對於10個多類別的資料集測試中，總共有8個資料集的分類正確率是最優秀的。雖然本研究提出隨機生成簡易貝氏模型的方式，在運算時間上不如其他訓練生成的方式，不過透過併行處理的特性，可以大幅縮短其中的運算時間。最後本研究提出隨機生成模型的方式，在將其作為簡易貝氏集成模型中的輔助角色時，可以獲得相比於其他訓練生成建構的簡易貝氏集成模型，有著更加優秀的分類表現，並且隨機生成模型時若搭配併行處理的特性之後，最終表現出來的分類效能值得令人期待。

Naïve Bayesian classifier is a well-known classification algorithm, and it has been widely used in numerous studies in the past because of its easy implementation, computational efficiency, and competitive classification performance. Since naïve Bayesian classifier is a robust algorithm, it is generally not the first choice in ensemble learning. The ensemble models built by using naïve Bayesian classifier have limit performance improvement with respect to single model induced by this algorithm.
In this study, a novel approach for constructing a classification model without training instances for naïve Bayes classifier is first proposed. The aim is to build an ensemble model composed of randomly generated base models which are not confined to the instances in a data set. The approach for generating independent classification models is employed to design two ensemble algorithms: Totally Random Ensemble Naïve Bayes (TR-ENB) and Bagging Random Ensemble Naïve Bayes (BR-ENB). The two algorithms are tested on 30 data sets to explore whether classification accuracy can be improved. The experimental results showed that TR-ENB and BR-ENB achieved the highest classification accuracy on 15 out of 20 bi-class datasets and 8 out of 10 multi-class data sets. Performance improvement can generally be achieved when the randomly generated base models are combined with some base models induced by the bagging approach.

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