隨著網路的進步，產生的資料量越來越多，如何有效的運用資料就變的很重要，這也讓資料探勘的技術更加精進成熟，然而遺漏值的問題一直存在於資料中很難避免，因此學者們利用統計方法、機器學習方法，做了許多填補遺漏值的研究，期許降低遺漏值對預測的影響，但在直接處理法的研究較少。
本研究因此提出一個基於深度學習中滑動視窗與邊界框概念的決策樹直接處理法：類深度決策樹(Deep Learning Oriented Decision Tree)，依照不同的視窗大小而切割資料集，建立多棵決策樹，最後再進行投票得出預測結果。本研究分成兩個實驗，實驗一主要是類深度決策樹與單一決策樹的比較，實驗二主要是類深度決策樹與其他處理遺漏值方法的比較，實驗一與二中又再分為（A）、（B）兩小實驗，探討測試資料集是否遺漏的差異。於實驗後的結果得知，在19維以上資料使用類深度決策樹直接處理不完整資料得到的分類正確率結果最好。相信這樣的貢獻能協助未來研究者能更恰當且有效率的處理遺漏值問題，能夠產生表現更佳的預測模型。

The advancement of network makes the amount of data produced increasing rapidly. How to use the data effectively becomes very important, which makes the data mining technology more sophisticated. However, the problem of missing values has always been difficult to avoid in the collected data. Therefore, scholars have used statistical and machine learning methods to do a lot of researches for the imputation of missing values, and hope to reduce the impact of missing values on predictions, but there are few studies focusing on another type of solution by directly handling the datasets with missing values.
Therefore, this thesis proposes a novel approach based on the concept of sliding window and bounding box in deep learning, namely “Deep Learning Oriented Decision Tree”. In this approach, the dataset is divided into several subsets according to different window sizes, and each subset is used to build a decision tree, resulting in decision tree ensembles, and the final prediction result is based on the voting method. There are two experimental studies in this thesis. Study 1 is based on a comparison between Deep Learning Oriented Decision Tree and a single decision tree, and Study 2 for a comparison between Deep Learning Oriented Decision Tree and other missing value imputation methods. Moreover, the testing data with missing values are also considered in the two studies. According to the results of the experiment, the proposed approach performs the best in terms of classification accuracy over higher dimensional datasets. It is believed that such a contribution can help future researchers to deal with missing value problems more appropriately and efficiently, and to produce better performing prediction models.

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