博碩士論文 964203018 詳細資訊




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姓名 徐聖堡(Sheng-Pao Hsu)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 考量屬性值取得延遲的決策樹建構
(Decision Tree Induction with Time Stamp of Information Acquisition)
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摘要(中) 資料探勘能夠從數以千萬計的資料中,探勘出有用的資訊和規則以協助決策者,找出目標顧客群並且做出更好的決策。分類是資料探勘其中一項應用非常廣泛的技術,依據已知的資料及其類別屬性來建立資料的分類模型,並以此預測其他未經分類資料的類別,而決策樹是最常使用到的一項分類模型,因為它產生的規則容易了解、且建立速度快又簡單;傳統決策樹必須要拿到完整的歷史資料,才可能生長決策樹,但是管理者希望能夠在較早的時間點,先行掌握未來可能的變化。
因此,本研究給定每筆資料中取得每個屬性的延遲時間(delay time),建立一棵考量time stamp 概念決策樹TStree,管理決策人員期望透過TStree 決策樹,找到決策時間點與分類正確率兼具的規則,先行掌握未來可能的變化。
本研究實驗結果顯示,本研究TStree 演算法只要取得部份資料,就能夠提早做出決策,並且能夠達到與傳統C4.5 相近的準確度。
摘要(英) Data mining can be used to discover the useful informations and rules from the tens of millions of data, and it can identify the target customers and make better decisions. Classification is a one of the data mining domain which a very wide range of application technologies, based on available information and categories of property to create a data classification model, and use this model to forecast class of the unclassified data, while the decision tree is the most commonly used to the a classification model, because it generates easy to understand rules, and fast to establishment and simple; the traditional decision tree need to gather a complete historical data, when it grow the decision tree, but the managers hope to make decision early in time.
Therefore, this study given the delay time (time stamp) for each attribute, and build a decision tree TStree which to consider the concept of time stamp, and the decision-makers hope to find the rules, it can be decision-making point early, and the nice rate of the classification accuracy through TStree.
The results show that we gather some information from the complete data set, then we can make decisions early through TStree Algorithm, and the accuracy of TStree algorithm is close to the accuracy of the traditional algorithm (i.e. ID3, C4.5, etc.)
關鍵字(中) ★ 提早決策時間
★ 延遲時間
★ 時間標籤
★ 分類
★ 決策樹
關鍵字(英) ★ decision time early
★ delay time
★ time stamp
★ classification
★ decision tree
論文目次 目錄 III
圖目錄 V
表目錄 VI
一、 緒論 1
1-1 研究背景 1
1-2 研究問題 2
1-3 預期結果 2
1-4 研究流程 2
1-5 論文架構 4
二、 文獻探討 5
2-1 C45 演算法 5
2-2 資料探勘 6
2-3 分類 6
2-4 決策樹 7
2-5 相關論文研究 7
2-5-1 提早執行決策 7
2-5-2 Cost-sensitive Analysis(成本敏感分析)相關決策樹文獻 8
2-5-3 Time Series(時間序列)相關決策樹文獻 9
2-5-4 Stock Market(股票市場)相關決策樹文獻 9
三、問題描述與相關定義 11
四、TSTREE 演算法 22
4-1 TSTREE 演算法基本概念 22
4-2 TSTREE 演算法架構 22
4-3 TSTREE 演算法範例說明 26
4-3-1 分類前要做Confidence、根節點類別 26
4-3-2 計算屬性值分類信心程度,即Confax(Dni) 27
4-3-3 篩選分類節點屬性的準則 28
4-3-4 篩選分類節點屬性的準則 29
4-3-5 決定根節點分類屬性:a2 30
4-3-6 重覆上述步驟生成TStree 決策樹 31
五、實驗評估 32
5-1 開發環境 32
5-2 資料來源 33
5-3 評估準則 33
5-4 資料前處理 34
5-4-1 中間屬性值的給定 34
5-4-2 類別標籤值的給定 34
5-4-3 認定類別標籤值時的發現 34
5-5 實驗結果 35
5-5-1 情境一:預測第7 個交易日股價 35
5-6 結果分析 39
六、結論與建議 40
6-1 結論 40
6-2 研究貢獻 40
6-3 未來研究 41
參考文獻 43
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指導教授 陳彥良(Yen-Liang Chen) 審核日期 2010-6-3
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