在過往的研究中，研究人員常常將研究重心放在找出資料的潛在類別，並透過資料的異質性結構定義出新類別，於是本研究提出基於同類別的異質性結構將各個原始類別拆成數個子類別以提高分類預測準確率。本研究以建立標籤內嵌樹的方法進行分類預測，此分類預測方法是先計算類別支配矩陣後，藉由此矩陣進行階層式分群，並由偽概似機率進行分類預測，而本研究比較使用此方法和常見分類器的分類預測表現差異，也比較常見分類器在使用子類別及原始類別的分類預測差異。研究結果顯示所提出的子類別方法，在異質性資料確實會擁有較高的預測正確率。另外，本研究發現在多數的分類器，以子類別預測能提升分類表現，但是需要考慮資料本身是否含有異質性結構。

In previous studies, researchers often focused their research on identifying potential categories of data and defining new categories through the heterogeneous structure of the data. Therefore, in this study, the original categories were divided into sub-categories based on the heterogeneous structure of the same category. Each sub-category is then classified and predicted by the method used in this study. This classification prediction method calculates the label dominance matrix, uses the matrix to group hierarchically, and uses the pseudo-likelihood probability to perform classification prediction. This research will compare the prediction accuracy rates of the common classifiers that use the original categories for classification prediction and this proposed method that uses the subcategories. The research results show that this proposed method will indeed have better results in some datasets. In addition, this study also compared whether the classification prediction using the split into sub-categories and the classification prediction using the original category will increase the accuracy of the prediction of various classifiers. It turns out that most of the classifiers have an improvement. Nevertheless, we need to consider if a heterogeneous structure exists in a category first before applying the proposed method.

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