不平衡資料意指資料中有特定類別的樣本個數特別少，造成各類別比例懸殊，此資料特性易使監督式學習的分類模型在訓練時，無法有效地學習少數類別的特徵，導致模型預測錯誤。為解決此問題，本研究嘗試對監督式學習方法Pseudo-Likelihood Ratio（PLR）進行兩種不同的調整，並分別提出調整後的分類模型；為了探討兩種分類模型在不同不平衡比例下的分類效能，本研究將調整後的兩個分類模型與原始PLR、KNN、SVM三個模型，對不同不平衡比例的資料集進行分類預測，以此比較五種模型在不同不平衡比例下的分類效能。最後研究顯示，本研究針對PLR所提出之改善方法，在不同資料集中的表現有所不同，但整體而言，對提升原始PLR分類效能是有所成效的。

Imbalanced data means that the number of specific categories in the data is very small, resulting in a disparity in the proportion of each category. This data characteristic easily makes the supervised learning classification model unable to effectively learn the features of a few categories during training, resulting in model prediction error. In order to solve this problem, this study attempts to make two different adjustments to the supervised learning method Pseudo-Likelihood Ratio (PLR), and propose the adjusted classification models respectively; in order to explore the classification accuracy of the two classification models under various imbalance ratios, the adjusted two classification models and the original PLR, KNN, and SVM were put into each imbalanced proportion of the five data sets for classification, so as to compare the classification performance of the five models. The result shows that the improvement methods proposed in this study for PLR have different performances in different data sets. Still, on the whole, it is effective in improving the classification performance of the original PLR.

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