傳統集成學習的研究多著重於解決基本模型間相依性過高的問題，相依性低的要求之所以產生，係因基本模型之間皆使用相同的訓練資料集所導致，即使透過訓練資料集進行隨機抽樣亦或是加權調整，仍無法有效改善。
故後續研究跳脫出訓練資料集的框架，不依賴訓練資料集進行學習，而是透過隨機的方式生成基本模型，並且在與袋裝法結合的實驗中，展現出良好的效能以及發展性，然而現有研究普遍缺乏將此方法與提升法結合的探討，主要原因為提升法的基本模型生成具有順序性，使其難以與隨機生成基本模型方法進行整合，故本研究設計兩種提升法結合隨機生成基本模型方法，使得在提升法架構下也可以運用隨機生成的基本模型，並以簡易貝氏分類器作為基本模型，由於其運算效率佳，且在隨機生成基本模型下，實作較為容易。
本研究旨在探討將提升法與隨機生成基本模型方法相結合後，是否能有效提升整體預測效能，並進一步與過往袋裝法混合隨機生成基本模型方法比較。研究結果顯示出將隨機生成基本模型方法與提升法結合後，平均正確率有上升，並且發現在提升法比起袋裝法有更高分類正確率的資料集上，繼續使用提升法結合隨機生成基本模型方法可以保持優勢。

This study explores whether randomly generated base models can be combined with boosting to improve classification performance. Traditional ensemble learning methods often suffer from high dependency among base models,because their base models are usually generated through similar learning processes. Previous studies have demonstrated that integrating randomly generated base models into bagging frameworks can effectively improve classification accuracy. However, little research has investigated their application in boosting. Therefore, this study proposes several approaches to combine randomly generated base models with boosting and examines their effectiveness.

何政賢，(2024) 以粒子群最佳化方法優化應用於二類別資料之隨機集成演算法。國立成功大學資訊管理研究所碩士班碩士論文
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班碩士論文
黃愉兒，(2025) 抽樣方法結合隨機生成法應用於分類不平衡資料集。國立成功大學資訊管理研究所碩士班碩士論文
蔡哲倫，(2024) 適用於多類別資料的含隨機生成模型之集成嵌套二分法。國立成功大學資訊管理研究所碩士班碩士論文
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