集成學習是透過特定的集成演算法產生多個基本模型，並將多個基本模型的預測結果整合成一個最終的預測結果。為了使用少量的基本模型就能維持良好的預測性能，集成挑選成為關注的焦點。過往基於最佳化的集成挑選方法，因為欲挑選的基本模型彼此間存在相關性，所以目標式會同時考慮正確率和多樣性。在有了隨機生成基本模型的方法後，能使基本模型彼此間相互獨立，直接滿足多樣性，因此，本研究提出的集成挑選法為基於正確率的最佳化模型，其適用於二類別資料集，從袋裝法和隨機生成演算法生成的基本模型中，挑選出最佳的基本模型組合。由於最佳化模型的限制式數量取決於資料筆數，因此建立資料過濾機制，以提升模型求解的效率。
從20個二類別資料集的結果表明，袋裝法、隨機生成法與袋裝混和隨機生成皆能透過資料過濾機制，過濾掉至少85%的最佳化模型的限制式。分類效能的部分，在隨機生成法和袋裝混和隨機生成的情況下，本研究的最佳化模型得到的平均分類正確率，皆優於同時考慮正確率和多樣性的比較文獻所得出的平均分類正確率。此外，袋裝混和隨機生成在平均分類正確率的表現最優異，證實了混和基本模型再進行集成挑選能有效提升集成模型的分類效能。即便增加基本模型的數量，本研究的最佳化模型在隨機生成法和袋裝混和隨機生成所得到的分類正確率依然能維持其優勢，證實了在基本模型間完全獨立或部分獨立的條件下，本研究的集成挑選方法之可擴展性。在執行時間方面，最佳化模型的求解時間會受決策變數和限制式數量多寡的影響，導致平均執行時間較長，運算效率相對不穩定，未來可採用啟發式演算法進行求解，以提升模型求解的效率。

Ensemble learning integrates multiple base models to improve classification performance. Ensemble selection is a popular technique to identify a better subset of base models to reduce model redundancy. Since the base models produced by current ensemble algorithms are dependent, ensemble selection methods need to consider both prediction accuracy and the diversity among base models. The base models generated randomly are independent, and considering diversity in ensemble selection is not necessary for this kind of base models. When there are only two class values in a data set, this study proposes an optimization model that considers only accuracy for ensemble selection. Since the number of constraints in the optimization model depends on the number of training instances, a data filtering mechanism is established to reduce model complexity for improving computational efficiency. The experimental results on twenty bi-class data sets show that when independent base models for naïve Bayesian classifier are involved in ensemble selection, the proposed method outperforms the optimization method proposed by a previous study, that considers both accuracy and diversity. The filtering mechanism can remove at least 85% constraints from the optimization model regardless of the method for ensemble selection.

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。
廖家德，(2024) 袋裝法和提升法分類準確率的差異分析。國立成功大學資訊管理研究所碩士班碩士論文。
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。
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