簡易貝氏分類器因為其做法簡單、計算效率高以及良好的分類效能，被廣泛的運用於許多領域中，但簡易貝氏分類器卻鮮少作為基本學習器用於建構集成學習，集成學習是功能強大的機器學習技術之一，透過訓練多個機器學習模型並將其輸出組合在一起的過程，以此來獲取比單個模型更好的分類表現。簡易貝氏分類器因為其本身的學習機制相對穩定，以至於訓練數據的微小變化不會導致分類結果的不同，雖使其對躁聲數據具有容忍的優勢，但同時也限制了使用簡易貝氏分類器做為基本模型建構成集成所能實現的改進，而過去通過使用各種集成方法構建簡易貝氏分類器的集合，卻很少可以實現或造成分類器的分類效能不佳。因此，本研究將修改簡易貝氏分類器的學習機制，採用隨機挑選屬性及選定一個類別加入廣義狄式分配參數進行基本模型的訓練，增加該分類方法的不穩定性以影響分類結果的差異性，同時提升整體分類情形。本研究所提出的分類器雖然運算時間較長，但僅針對隨機選定的類別方式求取參數，因此運算時間仍在合理的範圍內，並且實驗結果亦顯示，各資料集中分類正確率皆表現良好，在20個資料集的實驗中，共有15個分類正確率是最優秀的，並與隨機森林相比擁有更優越的效能且表現更為穩定。

Naïve Bayesian classifier is a widely applied classification algorithm because of its easy implementation, computational efficiency, and competitive prediction accuracy. Ensemble learning is a powerful technique, and an ensemble model constitutes of a set of base models. Naïve Bayesian classifier is seldom used in previous studies for ensemble learning since it is quite robust to the slight change in training data. In this study, the learning process of naïve Bayesian classifier is modified to induce base models. Bagging is first employed to obtain training sets in which attributes are randomly selected. Then a class value is randomly chosen to set its noninformative generalized Dirichlet priors so that the prediction accuracy is improved. Introducing priors can enhance the diversity among base models. The ensemble algorithm based on naïve Bayesian classifier is tested on 20 data sets, and the experimental results show that it achieves the highest accuracy in 15 data sets with respect to the original naïve Bayesian classifier and the ensemble naïve Bayesian classifier without priors. Its mean accuracy over the 20 data sets is higher than that of random forest.

姚靜姍，(2018)。簡易貝氏分類器在不平衡資料集上效能改善之研究。國立成功大學資訊管理研究所碩士班碩士論文。
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