不平衡資料在實務應用中十分常見，目前的研究文獻針對不平衡資料分類問題提出了各種方法，其中集成學習為廣受歡迎的方法之一。由於目前主流的集成學習方法主要都是從相同來源的訓練資料集，經過演算法的學習機制生成分類模型，使得模型的學習過程將有一定程度地受到原始資料分布的限制。因此，本研究透過隨機生成基本模型之集成方法為不平衡資料分類問題提供新的解決方案，根據過去的實驗結果，此方法跳脫了以往從資料訓練基本模型的步驟，並特別適合應用於二元資料集的預測分類問題，而不平衡資料通常也屬於這種情況。在隨機生成基本模型的集成方法中，由於篩選隨機模型的衡量指標對集成模型的結果相當重要，故本研究實驗了不同的模型衡量指標，包括單指標與雙指標組合為篩選門檻，實驗結果表明，使用雙指標對於提升集成模型分類表現的效果不明顯，同時需要花費較多的運算時間才能達成。因此，本研究後續使用三種不同單指標門檻值設定在30個資料集進行實證，結果表明，當不平衡資料影響模型的學習過程導致模型對少數類別的預測表現較差時，由於隨機模型在識別少數類別資料的能力上具有一定程度的優勢，使得將隨機模型加入集成模型中在多數測度上皆顯著地優於其他集成方法。然而，隨機生成基本模型之集成方法應用於不平衡資料分類問題時，在運算時間方面仍與過去的研究結果相同，相較於其他集成方法來得更耗時。

The data collected for many real-world applications are imbalanced, and the techniques for handling imbalanced data are thus proposed in these years. Ensemble learning is one of the most popular approaches for this purpose. Since most ensemble algorithms induce base models from the instance sets derived from the same training set, the predictions of the base models are not independent. The ensemble models built by this way is likely to favor the majority class and has a relatively poor performance on imbalanced data. In this study, ensemble algorithms that can randomly generate base models are introduced to improve this deficiency. Both single and double thresholds are set to filter base models, and the experimental results suggests that single threshold is more efficient and achieves similar performance to double thresholds. Single threshold for filtering base models is thus used to design two ensemble algorithms for processing imbalanced data. The experimental results obtained from 30 imbalanced data sets indicate that the ensemble algorithm with randomly-generated base models and the ones produced by the bagging approach significantly outperforms the other three ensemble algorithms regardless of the metric employed for performance evaluation. However, the proposed ensemble algorithm is computationally intensive because of the high computational cost for filtering base models.

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。
徐心縈. (2023)。用羅吉斯迴歸建構隨機分類模型之集成方法。國立成功大學工業與 資訊管理研究所碩士論文。
鍾佩真. (2022)。不平衡資料分類效能衡量指標之性質分析。國立成功大學資訊管理 研究所碩士論文。
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