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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 藍俊宏(Jyun-Hong Lan) | |
| dc.contributor.author | Chung-Cheng Huang | en |
| dc.contributor.author | 黃鍾承 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:46:20Z | - |
| dc.date.available | 2026-07-13 | |
| dc.date.available | 2022-11-24T03:46:20Z | - |
| dc.date.copyright | 2021-07-23 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81374 | - |
| dc.description.abstract | 錯誤偵測及預測的系統為許多先進製程中重要的分析環節，判斷的方法常以對資料分類或分群來判斷產品或在製品的正常或異常狀態。傳統上，利用機器學習演算法建立的良率分類模型，往往能夠達到很好的效果。然而類別間的比例失衡是實務上常見的資料特性，例如科技業製程中，一般僅有千分之一或甚至到百萬分之一的機率會出現不良品，因此大部分以整體分類正確率為目標的演算法很容易將全部的資料預測成良品，以達到極高的正確率，卻其實沒有真正學習到類別之間的差異性，更忽略了誤判不良品所帶來的極高成本，此類模型並無任何實質的應用價值。 近年針對上述問題的文獻多以資料擴充方法 (data augmentation) 或模型參數調整來解決外，也有透過先對資料特性進行分析，例如：不平衡比例、密集程度、類別間的重疊度或是在同類別中又存在著大小不同的群體等性質，再進行資料擴充，惟上述的擴充基礎皆根基於數值型變數。本論文承接此一基礎，轉發展非數值型變數，例如全為二元變數時，該如何進行資料擴充。我們利用漢明距離 (Hamming distance) 計算二元特徵間的相似度，透過少數類別和多數資料的互動關係提出一嶄新的上採樣 (oversampling) 方法，經降低資料中噪音的干擾和重疊度後，將新資料生成在少數類別之間，或是避開多數類別的混淆。 最後，本研究透過結合下採樣 (undersampling) 以及訓練集資料平衡比例的控制，對經過不同資料擴充方法的訓練集和常見的模型做多種組合的實驗分析，結果發現有進行上採樣的訓練集，其模型表現較佳；而對於極度不平衡且皆為類別變數的資料，透過本論文提出的方法亦能發現訓練集的改變對於最後的指標較有顯著的效果，而不同模型所帶來的影響則相對小。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:46:20Z (GMT). No. of bitstreams: 1 U0001-1307202101122500.pdf: 6023329 bytes, checksum: 3a280779bc7c86123fa292d9979fa9a6 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 中文摘要 i Abstract iii 第一章、緒論 1 第一節 研究動機與目的 1 第二節 研究架構 3 第二章、文獻探討 4 第一節 資料不平衡 4 (一) 資料形式及應用範疇 4 (二) 常見的問題及挑戰 5 第二節 代價敏感學習及特徵選擇 8 第三節 資料擴充 9 (一) 上採樣及下採樣 9 (二) 混合採樣法 11 第四節 模型及演算法 14 (一) 基本分類器 14 (二) 集成學習 14 (三) 優缺點及評估指標 15 第五節 文獻回顧摘要 17 第三章、研究方法 18 第一節 資料特性預判 22 第二節 資料擴充方法 25 第三節 模型評估架構 30 第四章、案例分析 33 第一節 資料集的特性 36 第二節 前處理資料 43 第三節 資料建模 50 第四節 預測結果的比較和關聯性分析 71 第五章、結論與建議 74 第一節 研究成果 74 第二節 未來研究方向 76 參考文獻 79 附錄A 84 附錄B 91 | |
| dc.language.iso | zh-TW | |
| dc.subject | 機器學習模型 | zh_TW |
| dc.subject | 不平衡資料 | zh_TW |
| dc.subject | 類別變數 | zh_TW |
| dc.subject | 資料重採樣 | zh_TW |
| dc.subject | 資料增能擴充 | zh_TW |
| dc.subject | 錯誤偵測與分類 | zh_TW |
| dc.subject | oversampling | en |
| dc.subject | machine learning algorithms | en |
| dc.subject | fault detection and classification | en |
| dc.subject | data augmentation | en |
| dc.subject | imbalanced data | en |
| dc.subject | categorical variables | en |
| dc.subject | undersampling | en |
| dc.title | 發展資料不平衡與類別變數限制下的生產良率分類模型 | zh_TW |
| dc.title | On the Development of Production Yield Classification Model Under Imbalanced Data and Categorical Variable Constraints | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪一薰(Hsin-Tsai Liu),陳正剛(Chih-Yang Tseng) | |
| dc.subject.keyword | 不平衡資料,類別變數,資料重採樣,資料增能擴充,錯誤偵測與分類,機器學習模型, | zh_TW |
| dc.subject.keyword | imbalanced data,categorical variables,oversampling,undersampling,data augmentation,fault detection and classification,machine learning algorithms, | en |
| dc.relation.page | 97 | |
| dc.identifier.doi | 10.6342/NTU202101424 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-16 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-07-13 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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