在過去的集成學習研究中，有研究透過隨機生成分類模型，使集成模型不再受限於原始資料的分布，比傳統集成模型更有效地提升預測能力，然而這類方法可能生成出預測能力較差的分類模型，因此必須先生成大量的分類模型，再透過門檻值進行篩選以確保集成模型的效能，這不僅會浪費計算資源，還顯著增加了運算時間，除了使用門檻值來篩選合適的分類模型外，數學規劃法和元啟發式演算法也可用於搜索適合的分類模型，然而數學規劃法僅適用於較小的解空間中，當解空間較大時其計算效率會顯著下降，且通常需要滿足特定的約束條件，相比之下，元啟發式演算法較適合處理解空間較大的複雜問題，能快速找到近似最佳解，並且有較高的計算效率。目前已有研究使用粒子群優化演算法，透過逐步優化隨機生成的分類模型，節省了生成大量隨機分類模型再進行過濾的時間，該方法在提升純隨機羅吉斯迴歸集成模型的運算效率頗有成效，然而在提升純隨機簡易貝氏集成模型的運算效率仍然有限。
為了改善粒子群優化演算法在純隨機簡易貝氏集成模型訓練中，運算效率提升有限的問題，本研究採用混合自適應粒子群優化與灰狼優化演算法，透過結合灰狼優化演算法的全局搜索能力，並引入自適應參數調整機制，以提升粒子在不同階段搜索的靈活性，加速分類模型的尋找，從而提升集成模型的運算效率與預測性能。實驗結果顯示，混合自適應粒子群優化與灰狼優化演算法應用於純隨機羅吉斯迴歸集成模型時，相較粒子群優化演算法在運算效率上提升約兩倍；與簡易貝氏集成模型相比，運算時間則可縮短約二至三倍，且能維持相同的分類效能，此外混合自適應粒子群優化與灰狼優化演算法在處理大資料集時能展現出更顯著的運算效率優勢，顯示其在實務應用中具備較高的可行性。

Recent studies in ensemble learning have shown that generating classification models randomly can improve predictive performance by avoiding the reliance on original data distribution. However, such approaches approach is computationally intensive, because it filters a base model from thousands of randomly-generated classification models. To address this, optimization techniques such as mathematical programming and metaheuristic algorithms have been used to search for suitable models. While mathematical programming is limited to small solution spaces and specific constraints, metaheuristic algorithms are better suited for complex problems with large search spaces. Previous research applied Particle Swarm Optimization (PSO) to incrementally improve random logistic regression ensemble models, significantly reducing runtime. However, its efficiency gains for naive Bayes ensembles remain limited. This study proposes a Hybrid of Adaptive Particle Swarm Optimization and Grey Wolf Optimization -Ensemble of Random Logistic Regression (APSOGWO), integrating global search capability and adaptive parameter tuning to enhance both search flexibility and convergence speed. Experimental results demonstrate that APSOGWO improves runtime efficiency by approximately twofold for Logistic Regression ensembles and reduces execution time by two to three times for Naïve Bayes ensembles, all while maintaining comparable classification performance. Moreover, APSOGWO demonstrates more significant efficiency advantages when handling large-scale datasets, indicating higher feasibility and potential for practical applications.

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。
徐心縈，(2023) 用羅吉斯迴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。
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