臺灣博碩士論文加值系統

本文主要探討干擾變數對分類樹準確度的影響。我們使用 CRUISE 、 LMT 、 QUEST 和 RF 四種分類樹方法,藉由不同的切割變數選取法建構出七種不同構造分類樹的演算法,
透過69筆實務資料並加上不同個數的干擾變數來了解其預測表現。平均而言,以 LMT 方法的預測表現最好, CRUISE 之 2D 的設定方法表現最差;在各種演算法受到干擾變數的影響方面,以 CRUISE 之 1D 的設定與 QUEST 以單變量為切割變數的設定方法最不容易受到干擾變數的影響,而 QUEST 之線性組合的變數為切割變數設定以及 RF 方法則最容易受到干擾變數的影響。另外,在單變量切割變數的選取方面,相對於 CRUISE 之 1D 與 2D 的設定和 QUEST 以單變量為切割變數的設定方法, LMT 方法會傾向選取切點個數較少的切割變數。

1 導論
2 演算法之介紹
2.1 CRUISE 方法
2.2 LMT 方法
2.3 QUEST 方法
2.4 RF 方法
3 實驗設計
3.1 資料集
3.2 演算法
4 實驗結果
4.1 無母數分析
4.2 實驗一
4.3 實驗二
4.4 實驗三
5 結論與建議
附錄
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