以往有關隨機生成模型的研究中，隨機生成簡易貝氏模型的方法，在二元分類的資料集上表現良好，但面對多類別問題時就表現的較不理想。因此，為了改善隨機生成簡易貝氏模型的方法，在多類別資料集表現不佳的情況，本研究想要透過二元化策略中的集成嵌套二分法，將多類別問題分解成多個不同二元分類問題，並在每個二元分類問題上，利用隨機生成簡易貝氏模型的方法，生成需要的基本模型，期望配合集成嵌套二分法的架構，讓隨機生成簡易貝氏模型的方法，原本在多類別資料集表現不佳的情況能得到改善。本研究還提出了另一種新的集成模型，在過往與嵌套二分法相關的方法，袋裝嵌套二分法中，加入隨機生成的模型，探討袋裝法搭配隨機生成的模型，是否能夠提升分類效能。
本研究提出的隨機生成簡易貝氏模型建構集成嵌套二分法，比起過往的隨機生成簡易貝氏模型建構集成模型，在多類別的資料集上，其平均分類正確率有大幅的提升，且隨著資料類別值個數的增加，兩個方法的分類正確率差距會越來越大。而與其他集成嵌套二分法進行比較，袋裝法混和隨機生成簡易貝氏模型建構集成嵌套二分法，其表現也最為亮眼，在30個多類別資料集的測試當中，在大多數資料集的分類正確率是表現最為優秀的，且其平均分類正確率也最高。雖然本研究所提出的隨機生成方法，運算時間會不如其他的集成嵌套二分法，但這部分能夠搭配併行處理的方式，縮短其運算時間。

The ensemble algorithms that can randomly generate base models for naïve Bayesian classifier generally have a better performance on bi-class data sets than the other ensemble algorithms. However, the performance of the ensemble models constituted by randomly generated base models becomes inferior on multi-class data sets. This study aims to decompose a classification task for multi-class data into multiple binary classification tasks by using the nested dichotomy strategy. Every base model in an ensemble algorithm with the nested dichotomy strategy is a binary tree in which every internal node has a classification model. The classification model in a binary tree can be either randomly generated or induced from an instance set. This study first proposes a way to build a binary tree in which every classification model is randomly generated for naïve Bayesian classifier. The way to build binary trees is then used design two ensemble algorithms for multi-class data. The experimental results on 30 multi-class data sets show that the one combining randomly-generated base models with the based models produced by the bagging approach has the best performance among six ensemble algorithms with the nested dichotomy strategy. However, it has a high computational cost with respect to the ensemble algorithms that induce classification models from instance sets.

黃中立（2023）。以簡易貝氏分類器隨機生成基本模型之集成方法。國立成功大學資訊管理研究所碩士論文，台南市。
徐心縈（2023）。用邏輯斯回歸建構隨機分類模型之集成方法。國立成功大學資訊管理研究所碩士論文，台南市。
陳昱嘉（2023）。隨機生成決策樹以進行集成學習之研究。國立成功大學資訊管理研究所碩士論文，台南市。
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