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| Title: | 利用決策樹插補遺失值之模擬研究
Missing Data Imputation with Classification and Regression Trees: A Simulation Study |
| Authors: | 陳政揚
Chen, Jheng-Yang |
| Contributors: | 張育瑋
Chang, Yu-Wei
陳政揚
Chen, Jheng-Yang |
| Keywords: | CART
決策樹
疊代插補
插補遺失值
CART
Decision trees
Iterative imputation
Missing data imputation |
| Date: | 2022 |
| Issue Date: | 2022-08-01 17:16:33 (UTC+8) |
| Abstract: | 遺失值的處理為資料分析前置處理之常見的議題。使用插補遺失值的方式讓資料成為完整資料再進行後續分析，是其中一種常見處理方法。本研究延續以決策樹插補遺失值的研究，比較了幾種文獻上現有的方法及一些小變形的插補表現。除了使用經典的CART演算法以外，也嘗試使用卡方檢定來找分割變數。有別於Rahman 與 Islam (2013) 提出的DMI方法，本文對於訓練資料的選取，不僅限於完全沒有遺失的觀測值，只要在要插補的變數沒有遺失值即可被選為訓練資料，可以更有效使用所有觀測值。在有遺失值的情境下建立決策樹，會遇到分割變數也有遺失值的問題，除了文獻上以平均數或眾數讓其通過的方法，本研究另外考慮兩種重抽的方式讓在分割變數為遺失值的元素通過。此外，參考文獻的一些疊代插補法，並將其運用於決策樹來插補遺失值：對於一筆資料，將各變數的遺失值疊代補值，直到收斂，這樣的方式可以避免文獻使用決策樹插補遺失值的通過問題，並且可以更有效率應用變數之間的關係。本研究使用模擬研究比較上述方法的優缺點，並且將這些方法應用至肝炎資料與信用卡核卡資料這兩筆實際資料。
Dealing with missing values is an issue in data process before we conduct data analysis. It is a popular approach to impute missing data so that we have a complete data set for further data analysis. The current study continues the studies of imputing missing data using decision trees, we modify some methods in the literature and compare their imputation performance. In addition to the CART algorithm, chi-square tests are performed to find the split variable. Different from the DMI method proposed by Rahman and Islam (2013), the composition of the training data set is not limited to those observations without any missing values, but all the observations whose response variable is available are used for training in the current study. Through the modification, we tried to make most use of all the observed data. Besides, one would encounter the issue that there is a missing value in a split variable when building a decision tree using a data set with missing values. In addition to the imputation using the mean or mode so that all elements are able to be available down the tree in the literature, the current study proposes two resampling methods. Lastly, we incorporate some iterative imputation methods in the literature with decision trees. For a given data set, each variable with missing values will be imputed iteratively until convergence in the iterative imputation method. Hopefully, the relationship between variables can be utilized more effectively. We compare all the methods in some simulation studies. These methods are also applied to two real data sets: Hepatitis Data Set and Credit Approval Data Set. |
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| Description: | 碩士
國立政治大學
統計學系
109354019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109354019 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202200953 |
| Appears in Collections: | [統計學系] 學位論文
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