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研究生:黃慧慈
研究生(外文):Huang, Hui-Tzu
論文名稱:田口實驗設計於特徵擷取與參數搜尋之應用
論文名稱(外文):Taguchi Method in Feature Selection and Parameter Determination
指導教授:白炳豐白炳豐引用關係
指導教授(外文):Pai, Ping-Feng
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:76
中文關鍵詞:疾病診斷特徵擷取田口實驗設計支援向量機螞蟻演算法
外文關鍵詞:Disease DiagnosisFeature SelectionTaguchi MethodSupport Vector MachineAnt Algorithm
相關次數:
  • 被引用被引用:2
  • 點閱點閱:233
  • 評分評分:
  • 下載下載:50
  • 收藏至我的研究室書目清單書目收藏:2
摘要
傳統的醫學診斷大多是醫生依據以往的經驗,進行疾病的診斷。隨著醫療系統的電腦化,隱藏資料庫中的資訊愈來愈豐富。倘若透過先進的分析技術,可找出病症特徵和疾病間的相關性,輔助醫生提高診斷之準確率。許多的研究已證實出應用機器學習的技術,對於提高疾病診斷的正確性有相當顯著的幫助。
支援向量機近年來已廣泛地應用於分類及預測的問題上,在使用支援向量機時,會面臨二個問題,一是輸入特徵之選擇,另一則是支援向量機之參數調整,如何同時可將特徵選擇最佳化與參數調整,將是一重要的課題。
本研究以UCI公開之醫療資料庫為基礎,使用特徵擷取方法,並透過田口實驗設計進行參數設計與特徵之篩選,以螞蟻演算法找出支援向量機之參數,建構出疾病診斷模式,也同時解決了支援向量機的二大問題。實驗結果顯示,本研究所提出的方法可以使用較少的特徵屬性及較佳的參數,提高整體分類之準確程度,並與倒傳遞類神經網路進行診斷成效之優劣比較。

關鍵詞:疾病診斷、特徵擷取、田口實驗設計、支援向量機、螞蟻演算法
Abstract
In traditional medical diagnosis, doctors usually diagnose patients according to their experiences. Following the computerization of medical systems, there are more and more tacit information in it. If we can use the advanced method of data analysis to find the correlation between the disease feature and disease, it will help doctors for enhancing the accuracy of disease diagnosis. Many researchers have been confirmed that machine learning technology can improve disease diagnosis accuracy significantly.
SVM (Support Vector Machine) has been generally used in classification and forecasting issues. There are two problems when using SVM, one is choosing the input feature, the other is parameters setting of SVM. How to find optimal feature selection and parameters setting at the same time will be an important topic.
An approach we proposed is based on taguchi method for feature selection and parameter design, then applying ant algorithm to find the parameters of SVM. The used databases are medical datasets form University of California at Irvine (UCI) Machine Learning Repository. The experiment results show our proposed approach uses less features and better parameters to improve total accuracy and comparable with BPNN (Back Propagation Neural Network).

Keywords:Disease Diagnosis、Feature Selection、Taguchi Method、Support Vector Machine、Ant Algorithm
誌謝 I
摘要 II
Abstract III
圖目錄 VI
表目錄 VII
第一章 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 2
1-3 研究範圍與限制 2
1-4 研究流程 2
第二章 文獻探討 4
2-1 特徵擷取 4
2-2 疾病診斷與分類之應用 6
2-3 田口實驗設計 11
2-4 螞蟻演算法 13
2-5 支援向量機 17
2-6 倒傳遞類神經網路 19
第三章 研究方法與流程 21
3-1 疾病診斷分類之架構與流程 21
3-2 特徵擷取方法 22
3-2-1 田口實驗設計 22
3-2-2 因素分析 25
3-2-3 主成份分析 27
3-3 田口實驗設計的應用 29
3-3-1 以田口實驗設計選擇類神經網路最佳參數 29
3-3-2 以田口實驗設計選擇螞蟻演算法最佳參數 29
3-4 預測分類模式 31
3-4-1 倒傳遞類神經網路 31
3-4-2 支援向量機 33
3-5 田口螞蟻支援向量機 38
3-6 績效評估 42
第四章 實例分析 44
4-1 資料蒐集及正規化 44
4-2 特徵擷取實驗結果 45
4-2-1 田口實驗設計 45
4-2-2 因素分析 48
4-2-3 主成份分析 49
4-3 分類模式實驗結果 51
4-3-1 以田口實驗設計選擇倒傳遞網路最佳參數及其結果 51
4-3-2 螞蟻支援向量機之分類結果 54
4-3-3 田口螞蟻支援向量機之分類 55
第五章 結論及未來研究 57
5-1 實驗結果及結論 57
5-2 後續研究與建議 64
參考文獻 65
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