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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
| dc.contributor.author | Yu-Hsin Chang | en |
| dc.contributor.author | 張鈺欣 | zh_TW |
| dc.date.accessioned | 2021-07-10T21:39:24Z | - |
| dc.date.available | 2021-07-10T21:39:24Z | - |
| dc.date.copyright | 2020-08-24 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-12 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/76884 | - |
| dc.description.abstract | 本研究開發有效率的可解釋自動化分層預測方法，當多個類別型變數及數值型變數間有高維度且非線性的交互作用時，此一預測方法可對單一類別變數分層進行全自動且有效的分層建模、選模、及預測，不僅兼具準確度更能深入剖析資料探討其模型可解釋性，並探討資料集大小變化對選模預測效能影響。 在工業與商業應用領域的混合數據集中，處裡類別變數和數值變數之間具有複雜交互作用的是常見的問題，以製造系統為例，系統中的生產率同時受到不同的類別變數與和數值變數共同影響，例如不同機器類型與產品類別屬性及其數值變數間之複雜有交互作用，進而同時影響生產率。本研究運用機器學習方法提高具有複雜交互作用混合資料集的預測效能並透過選模方法探討模型的透明度與可解釋性。與過往的分層回歸或聚類方法相比，本研究所需更少的訓練資料和計算成本。將資料集劃分為具有不同類別屬性組合的資料子集，並生成多個預測模型，運用訓練和驗證資料集指標作為選擇預測模型依據，並以一階段和兩階段模型選擇方法選出最穩健的預測模型，此外透過選模結果探討模型解釋性並研究資料集大小變化對選模預測效能影響。數值分析結果顯示，在半導體製封測的混合資料集中，與回歸模型相比，運用分層組合的模型選擇方法可以觀察到均方根誤差值降低30％以上，交叉驗證測試結果表示，與優化超參數的XGBoost模型相比，其預測準確性提高7.5％。此外，提出的模型選擇方法與其他回歸或ML預測方法相互兼容，可用於提高預測混合資料集現有方法的模型透明度與可解釋性。 | zh_TW |
| dc.description.abstract | Mixed Datasets with complex interactions between categorical and numerical attributes are common in engineering and business applications. For example, production rates in manufacturing systems are jointly influenced by several categorical and numerical attributes, such as machine and product types and their numerical attributes. This study aims to improve the prediction performance and transparency of mixed datasets with complex interactions using machine learning (ML) methods. The proposed method requires lesser data and computational effort than existing hierarchical or clustering regression methods. Multiple prediction models can be generated by partitioning a dataset into subsets with different categorical attribution combinations. One- and two-stage model selection methods are proposed to use the training and validation datasets in selecting better models among all the prediction models. Numerical results demonstrate the potential of the model selection approach in a mixed dataset from a semiconductor manufacturer. In comparison with regression models, more than 30% reduction in root mean square error is observed using the proposed model selection approach. The cross-validation test results also demonstrated a 7.5% improvement in accuracy against the properly tuned XGBoost models. Moreover, the proposed model selection approach is compatible with other regression or ML prediction methods and can be used to improve the model’s transparency of any existing methods on mixed datasets. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T21:39:24Z (GMT). No. of bitstreams: 1 U0001-1108202023464000.pdf: 2691067 bytes, checksum: bd90f839907c4bd1c88add58352a5843 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 I 中文摘要 II ABSTRACT III 目錄 IV 圖 目 錄 VII 表 目 錄 VIII 第一章 緒論 1 1.1 研究背景與動機 1 1.1.1 混合資料集預測問題 1 1.1.2 少量多樣化的產品特性 2 1.1.3 分層式建模之應用 2 1.1.4 機器學習模型的透明度與可解釋性問題 3 1.1.5 研究背景與動機結論 4 1.2 研究目的 5 1.3 研究方法與流程 5 第二章 文獻回顧 7 2.1 階層式分群/階層式迴歸方法及其應用 7 2.1.1 多項式回歸/ 分段回歸 7 2.1.2 階層線性模型 8 2.1.3 分層聚類法Hierarchical Clustering 8 2.2 特徵選擇方法及其應用 9 2.2.1 特徵選擇介紹 9 2.2.2 特徵選擇缺點 10 2.3 集成學習方法及其應用 11 2.3.1 引導聚集算法(Bagging) 11 2.3.2 提升方法(Boosting) 11 2.3.3 堆疊法(Stacking) 12 2.3.4 XGBoost演算法 12 2.4 機器學習模型的透明度與可解釋性問題 13 2.5 文獻探討小節 14 第三章 組合資料集與模型定義 15 3.1 原始資料集描述與資料前處裡 15 3.2 基礎、部分與全組合定義與相對應資料集 16 3.2.1 基礎類別屬性組合定義與相對應之資料集 16 3.2.2 部分類別屬性組合定義與相對應之資料集 17 3.2.3 全類別屬性組合定義與相對應之資料集 19 3.3 基礎、部分與全組合模型定義 21 3.3.1 基礎類別屬性組合預測模型 21 3.3.2 部分類別屬性組合預測模型 21 3.3.3 全類別屬性組合預測模型 21 3.4 基礎、部分與全組合模型訓練流程 22 3.5 XGBOOST模型超參數設定 25 第四章 預測模型選擇方法 26 4.1 定義選模指標 26 4.2 一階段與兩階段模型選擇方法之評估指標 27 4.2.1 一階段模型選擇方法(A, B, n) : 27 4.2.2 兩階段模型選擇方法(A, B, n, C, m): 27 4.3 一階段與兩階段模型選擇方法之評估指標 28 第五章 資料集驗證與數值分析 31 5.1 資料集介紹 31 5.2 一階段與兩階段模型選擇演算法驗證 34 5.2.1 半導體資料集模型選擇方法效能比較 34 5.2.2 鑽石資料集模型選擇方法效能比較 42 5.3 模型透明度與可解釋性 49 5.4 資料筆數對選模方法預測效能影響 51 5.5 數值分析小結 54 第六章 結論與未來研究方向 55 6.1 結論 55 6.2 未來研究方向 55 參考文獻 56 | |
| dc.language.iso | zh-TW | |
| dc.subject | 分層分群 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 預測方法 | zh_TW |
| dc.subject | 分層方法 | zh_TW |
| dc.subject | Manufacturing | en |
| dc.subject | Hierarchical method | en |
| dc.subject | Expert systems | en |
| dc.subject | Hierarchical Clustering | en |
| dc.subject | Prediction methods | en |
| dc.subject | Regression analysis | en |
| dc.title | 混合資料集之分層組合預測模型 | zh_TW |
| dc.title | Using Partial Combination Prediction Models for Mixed Datasets | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 喻奉天,胡毓仁,余承叡 | |
| dc.subject.keyword | 分層方法,分層分群,預測方法,機器學習, | zh_TW |
| dc.subject.keyword | Hierarchical method,Hierarchical Clustering,Prediction methods,Regression analysis,Manufacturing,Expert systems, | en |
| dc.relation.page | 61 | |
| dc.identifier.doi | 10.6342/NTU202003028 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-12 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| Appears in Collections: | 工業工程學研究所 | |
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| U0001-1108202023464000.pdf Restricted Access | 2.63 MB | Adobe PDF |
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