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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳正剛 | |
dc.contributor.author | Kang-Heng Ma | en |
dc.contributor.author | 馬康恆 | zh_TW |
dc.date.accessioned | 2021-06-08T00:08:54Z | - |
dc.date.copyright | 2013-08-23 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-09 | |
dc.identifier.citation | Altman, D. G., & Bland, J. M. (1994). Diagnostic tests. 1: Sensitivity and specificity. BMJ: British Medical Journal, 308(6943), 1552.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees Belmont. CA: Wadsworth International Group. Dwyer, A. J. (1996). In pursuit of a piece of the ROC. Radiology, 201(3), 621-625. Frates, M. C., Benson, C. B., Charboneau, J. W., Cibas, E. S., Clark, O. H., Coleman, B. G., ... & Tessler, F. N. (2005). Management of Thyroid Nodules Detected at US: Society of Radiologists in Ultrasound Consensus Conference Statement1. Radiology, 237(3), 794-800. McClish, D. K. (1989). Analyzing a portion of the ROC curve. Medical Decision Making, 9(3), 190-195. Pepe, M. S. (1997). A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika, 84(3), 595-608. Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32-35. 巫信融,2009,多層判別分析及其應用,國立台灣大學工業工程學研究所碩士論文。 賴淑俐,2010,多層判別分析理論與方法擴張及其於腫瘤診斷上的應用,國立台灣大學工業工程學研究所碩士論文。 劉中維,2009,甲狀腺腫瘤超音波特徵之量化與效力分析,國立台灣大學工業工程學研究所碩士論文。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17364 | - |
dc.description.abstract | CART是最常見的分類樹,其中的每個節點都有兩個子節點。另外一種分類樹多層判別分析有別於CART,每一層可能有兩個或三個節點,其中一節點為未分類資料,再由這一個未分類節點的資料繼續利用其他屬性分割展開新的一層。通常這些分類樹使用Gini Index作為分割的準則,然而在某些特定資料類型,Gini Index無法有效率地做分類。此外,傳統分類樹在找尋屬性時,沒有考慮屬性分辨單一類別的能力,而是考慮同時分辨出兩類別的能力,因此常錯失對分類有幫助的屬性。
在本研究裡,我們先在理論探討中比證明Gini Index在特定資料時,會選擇不合理的切點,進而提出利用Gini Index與Youden’s Index在一個屬性中找兩個切點的方法。在每一個節點進入演算法時,本模型利用接收者操作特徵曲線的部分線下面積(partial area under curve,簡稱PAUC)的統計檢定結果找出適合的屬性,並決定該節點是否有分割的必要。若需再分割,則再次利用接收者操作特徵曲線的部分線下面積判斷此節點該分割為兩個或是三個子節點,最後用Gini Index或Youden’s Index找出分割的切點。 為了驗證此模型,我們利用了數個模擬案例與腫瘤分類的實例來測試,比較新判別模型與原始的CART和多層判別分析的判別結果,驗證此判別模型效能。從案例驗證的結果,可以看出利用接收者操作特徵曲線的部分線下面積之分類模型可以較有效率的分類資料。 | zh_TW |
dc.description.abstract | The Classification and Regression Tree (CART) is the most commonly used classification tree consisting of a hierarchy of decision nodes. Each decision node in CART can only be split into two child-nodes. To construct a more effective tree, an alternative classification tree called multi-layer classifier can be built with each node split into up to three child-nodes. Among the child-nodes, one is called undetermined node with instances clearly classified. The tree is then further grown by splitting the undetermined node into a new layer of two or three nodes until a stop criterion is reached. Both CART and multi-layer classifier use the Gini Index as the criterion for cutoff point and attribute selection. However, for certain types of classification problems, the Gini Index appears to be inefficient and thus results in falsely identified attributes.
In this research, we will first discuss and prove theoretically the weakness of the Gini index. We will then propose a method using the Youden’s index as the criterion. In the proposed algorithm, when a node is to be split, one feature is selected by comparing the test results of partial areas under receiver operating characteristic curve(PAUC). After a feature is picked, the algorithm will also use the PAUC to determine the number of child-nodes required and the corresponding cutoff point(s) by comparing the values of the Youden’s index. The test results of PAUC will also determine whether the tree construction is to be terminated. Simulated and actual cases are used to demonstrate and verify the proposed method and its superior discriminating capability over the original CART and the multi-layer classifier. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T00:08:54Z (GMT). No. of bitstreams: 1 ntu-102-R00546013-1.pdf: 6203476 bytes, checksum: 5ecf3fe317aab0fd7e03eca7d98c0667 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 研究背景 1 1.2 研究動機與研究目標 2 1.3 論文架構 3 Chapter 2 文獻探討 4 2.1 接收者操作特徵曲線 4 2.1.1 ROC curve之建立 4 2.1.2 ROC curve之線下面積與部分線下面積 7 2.2 接收者操作特徵曲線線下面積之統計檢定 9 2.2.1 參數接收者操作特徵曲線 9 2.2.2 部分線下面積之估計與統計檢定 10 2.3 分類樹 12 2.3.1 CART 12 2.3.2 多層判別分析 14 Chapter 3 理論探討 19 3.1 利用接收者操作特徵曲線與Youden’s Index找尋切點 19 3.2 Gini Index 與 Youden’s Index的比較 27 3.2.1 Gini index 與 Youden’s index的簡化 27 3.2.2 Gini index 與 Youden’s index的切點選擇差異 28 Chapter 4 利用接收者操作特徵曲線強化分類樹 32 4.1 模型架構 32 4.2 建構模型的完整流程 33 4.2.1 以參數型ROC curve建構CART 33 4.2.2 以參數型ROC curve建構多層判別分析 40 4.2.3 以經驗型 ROC curve建構多層判別分析 41 4.2.4 分割節點的流程 46 4.3 判別分析的比較 47 Chapter 5 實際案例之腫瘤診斷 60 Chapter 6 結論與未來研究建議 78 REFERENCE 80 | |
dc.language.iso | zh-TW | |
dc.title | 利用接收者操作特徵曲線建構分類樹之研究與應用 | zh_TW |
dc.title | Construction and Applications of Classification Trees with Receiver Operating Characteristic Curve | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉仁沛,洪弘 | |
dc.subject.keyword | 分類樹,接收者操作特徵曲線,部分線下面積, | zh_TW |
dc.subject.keyword | classification tree,CART,ROC curve, | en |
dc.relation.page | 80 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2013-08-09 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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