影響集成模型分類效能的關鍵因素為基本模型的準確率和多樣性，為了提升集成模型的多樣性，過去的研究在模型訓練過程中使用多個不同的子訓練集，然而，此方法對於提升多樣性的效果存在一定的上限。為了改善此問題，本研究以羅吉斯迴歸模型為基本模型，提出了一種新的集成模型為ERLR（Ensemble of Random Logistic Regression）。與傳統的基於資料集的訓練方法不同，研究跳脫以往從資料集訓練基本模型的步驟，並考慮到羅吉斯迴歸模型的特性，對迴歸係數和屬性選擇進行檢測，設計了四種不同的實驗組合，實驗結果表明，在大多數資料集上，使用全部屬性生成隨機標準化係數可以獲得較好的分類表現。
此外，本研究進一步提出了一個集成模型，稱為EMRLR（Ensemble of Mixed Random Logistic Regression），EMRLR與袋裝法訓練基本模型的組合，希望同時提升集成模型的準確率和多樣性。在20個資料集上進行實驗，結果表明，相對於其他集成方法，加入隨機模型的方法能夠顯著提升集成模型的分類效能。在執行時間的方面，雖然隨機分類模型較其他集成方法多出許多，往後可以透過平行運算，減少單一伺服器的運行程式的時間，從而使得隨機分類模型在時間成本與分類效能上都能獲得更好的表現。

The classification performance of an ensemble model is primarily determined by the accuracy and diversity of its base models. Previous studies used multiple subsets derived from the same training set to induce base models. The diversity among base models is thus limited in improving the performance of ensemble models. This study aims to propose a new ensemble algorithm, called ERLR (Ensemble of Random Logistic Regression), that uses the logistic regression to induce base models without training data. In generating a base model, attributes can be partial or full, and the coefficients in a linear regression model can be ordinary or standardized. The experimental results on 20 data sets suggest that using all attributes and standardized coefficients can have a higher accuracy in most data sets. This study also proposes an ensemble algorithm, called EMRLR (Ensemble of Mixed Random Logistic Regression), that combines the base models produced by the ERLR algorithm and the bagging approach. Experimental evaluations conducted on the same 20 data sets showed that algorithm EMRLR can significantly outperform the other three ensemble algorithms. However, the computational cost of EMRLR is the highest. This deficiency can be overcome by applying the parallel-processing technique.

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