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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳文方(Wen-Fang Wu) | |
| dc.contributor.author | Chun-Tai Yen | en |
| dc.contributor.author | 顏均泰 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:04:20Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-23T09:04:20Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-29 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79582 | - |
| dc.description.abstract | 2020年起全球COVID-19疫情擴散，迫使工廠實施作業分流，造成生產線上人力的短缺，而製造業供應鏈體系也出現難以預料的趨勢變化，迫使企業必須加速實現數位轉型。數位轉型帶來大量的數據化過程，機器學習儼然成為處理工業數據重要方法之一，其中特徵工程更是決定機器學習結果最關鍵的程序。傳統特徵工程是運用有經驗人員的領域知識來構建數據特徵，其程序作法對企業而言是繁瑣且耗時的，因此發展自動化特徵工程為目前發展主要趨勢。本研究提出One-Stop Auto-Feature Engineering (O-SAFE)，以特徵生成、特徵選擇和特徵評估來產生自動化特徵工程架構，不僅能在特徵生成上兼顧數值型及類別型特徵，也能同時包含時間、領域和關聯等特徵資料樣態。在特徵選擇上，則以統計分析方法處理因自動化特徵生成過程所衍生之大量新特徵，增加篩選有效特徵之執行速度。O-SAFE更重要的是以特徵評估方法來解決特徵生成後，過多高度關聯的特徵影響模型產生過度擬合，使得訓練資料集在模型訓練上準確度高、測試資料集於驗證時卻準確度大幅降低的問題。本研究以一組製造設備的實際數據與二組開源數據來驗證所提出之O-SAFE，其結果顯示所生成之有效特徵數量比傳統方法多出近一倍，準確度也比專家人工處理的結果提高8.8%；O-SAFE在特徵選擇與特徵評估做法上，避免特徵訓練產生的過度擬合問題，也比其他自動化特徵工程結果提高準確率10.7%。綜整O-SAFE在特徵生成類別型特徵的數量上、特徵選擇的執行速度與特徵評估解決特徵過度擬合等問題上，皆能顯示其優越性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:04:20Z (GMT). No. of bitstreams: 1 U0001-1509202117313400.pdf: 4904371 bytes, checksum: d9b5ed32be5b961418cae13789fe45b7 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 I 中文摘要 II Abstract III 目錄 V 圖目錄 VII 表目錄 IX 1 緒論 1 1.1 研究背景 1 1.2 現有問題 6 1.3 解決方案 12 2 文獻探討 13 2.1 自動化機器學習(AutoML) 13 2.2 特徵生成 16 2.3 特徵選擇 22 2.3.1 基於相以性的方法 22 2.3.2 基於資訊理論的方法 24 2.3.3 基於稀疏學習的方法 25 3 研究方法 26 3.1 概念架構 26 3.2 方法設計 30 3.2.1 O-SAFE特徵生成 30 3.2.2 O-SAFE特徵選擇 37 3.2.3 O-SAFE特徵評估 42 3.3 研究步驟 51 4 實驗設計、結果與討論 53 4.1 實驗設計 53 4.1.1 實驗1：以實際工廠的設備數據為驗證資料 54 4.1.2 實驗2：以泛用型開源數據為驗證資料 63 4.1.3 實驗3：以類別型資料的開源數據為驗證資料 66 4.2 實驗流程 70 4.2.1 實驗1-3：資料集D + O-SAFE 74 4.2.2 實驗1-4：資料集D + Autofeat 79 4.2.3 實驗2-1：資料集P + H2O 81 4.2.4 實驗2-2：資料集P + O-SAFE 83 4.2.5 實驗2-3：資料集P + Autofeat 87 4.2.6 實驗3-1：資料集W + H2O 89 4.2.7 實驗3-2：資料集W + O-SAFE 90 4.2.8 實驗3-3：資料集W + Autofeat 95 4.3 實驗結果討論 97 4.3.1 實驗1：結果討論 97 4.3.2 實驗2：結果討論 100 4.3.3 實驗3：結果討論 101 5 結論與建議 104 5.1 結論 104 5.2 後續建議與改善 106 參考文獻 107 | |
| dc.language.iso | zh-TW | |
| dc.title | O-SAFE：應用於機器學習之自動化特徵工程建構 | zh_TW |
| dc.title | Construction of O-SAFE (One-Stop Auto-Feature Engineering) for Machine Learning | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 蔡孟勳(Hsin-Tsai Liu),蔡曜陽(Chih-Yang Tseng),吳政鴻,藍俊宏,王世明,欉振坤 | |
| dc.subject.keyword | 機器學習,自動化機器學習,特徵工程,工業4.0,數位轉型, | zh_TW |
| dc.subject.keyword | Machine learning,AutoML,Feature Engineering,Industry 4.0,Digital transformation, | en |
| dc.relation.page | 111 | |
| dc.identifier.doi | 10.6342/NTU202103196 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-09-30 | |
| 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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