Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89425
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor林守德zh_TW
dc.contributor.advisorShou-De Linen
dc.contributor.author林語萱zh_TW
dc.contributor.authorYu-Hsuan Linen
dc.date.accessioned2023-09-07T16:57:25Z-
dc.date.available2025-01-01-
dc.date.copyright2023-09-11-
dc.date.issued2023-
dc.date.submitted2023-08-02-
dc.identifier.citationIndrė Žliobaitė, Mykola Pechenizkiy, and Joao Gama. An overview of concept drift applications. Big data analysis: new algorithms for a new society, pages 91–114, 2016.
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, and Guangquan Zhang. Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, 31(12):2346–2363, 2019.
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1–37, 2014.
Leandro L Minku, Allan P White, and Xin Yao. The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Transactions on knowledge and Data Engineering, 22(5):730–742, 2009.
Geoffrey I Webb, Roy Hyde, Hong Cao, Hai Long Nguyen, and Francois Petitjean. Characterizing concept drift. Data Mining and Knowledge Discovery, 30(4):964– 994, 2016.
X. Zhu. Stream data mining repository, 2010.
Nien-En Sun. Predictive ensemble learning based on the dynamic predictor for concept drift scenarios. https://hdl.handle.net/11296/9q6474, 2022.
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1–37, 2014.
Ryan Elwell and Robi Polikar. Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks, 22(10):1517–1531, 2011.
Dariusz Brzezinski and Jerzy Stefanowski. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Transactions on Neural Networks and Learning Systems, 25(1):81–94, 2014.
Yu Sun, Ke Tang, Zexuan Zhu, and Xin Yao. Concept drift adaptation by exploiting historical knowledge. IEEE Transactions on Neural Networks and Learning Systems, 29(10):4822–4832, 2018.
Michal Kolárik, Martin Sarnovský, and Ján Paralič. Diversity in ensemble model for classification of data streams with concept drift. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pages 000355–000360, 2021.
Peng Zhao, Le-Wen Cai, and Zhi-Hua Zhou. Handling concept drift via model reuse. Machine Learning, 109:533–568, 2020.
Paulo Almeida, Luiz Soares de Oliveira, Alceu Jr, and Robert Sabourin. Adapting the dynamic classifier selection for concept drift scenarios. Expert Systems with Applications, 104:67–85, 08 2018.
Atsutoshi Kumagai and Tomoharu Iwata. Learning future classifiers without additional data. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.
Atsutoshi Kumagai and Tomoharu Iwata. Learning non-linear dynamics of decision boundaries for maintaining classification performance. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), Feb. 2017.
Atsutoshi Kumagai and Tomoharu Iwata. Learning dynamics of decision boundaries without additional labeled data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1627– 1636, 2018.
Kanishka Khandelwal, Devendra Dhaka, and Vivek Barsopia. Predicting future classifiers for evolving non-linear decision boundaries. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 628–643. Springer, 2020.
Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian, et al. Ddg-da: Data distribution generation for predictable concept drift adaptation. arXiv preprint arXiv:2201.04038, 2022.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
Viktor Losing. driftdatasets. https://github.com/vlosing/driftDatasets, 2018.
Lobo J. L. Synthetic datasets for concept drift detection purposes, 2020.
Viktor Losing, Barbara Hammer, and Heiko Wersing. Knn classifier with self adjusting memory for heterogeneous concept drift. In 2016 IEEE 16th international conference on data mining (ICDM), pages 291–300. IEEE, 2016.
Vinícius MA Souza, Diego F Silva, João Gama, and Gustavo EAPA Batista. Data stream classification guided by clustering on nonstationary environments and extreme verification latency. In Proceedings of the 2015 SIAM international conference on data mining, pages 873–881. SIAM, 2015.
K. Bache and M. Lichman. UCI machine learning repository, 2013.
Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer, and Ramón Huerta. Chemical gas sensor drift compensation using classifier ensembles. Sensors and Actuators B: Chemical, 166-167:320–329, 2012.
Albert Bifet, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer. Moa: Massive online analysis. J. Mach. Learn. Res., 11:1601–1604, aug 2010.
Leif E Peterson. K-nearest neighbor. Scholarpedia, 4(2):1883, 2009.
Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 2017.
Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin. Catboost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363, 2018.
-
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89425-
dc.description.abstract在許多實際應用中,資料以資料流的形式隨時間收集並用於訓練機器學習模型。然而,在動態變化的真實環境中,資料分布常常發生變化,這種現象被稱為概念漂移。為了應對概念漂移,先前的研究主要集中在適應模型到最新的概念上。此外,在許多情況下,存在預測驅動概念漂移的潛在因素並預測未來概念的可能性。先前的研究已經處理了針對迴歸任務的真實世界概念漂移預測和針對分類任務的漸進式概念漂移預測。然而,針對分類任務的真實世界概念漂移預測的研究仍然有限。本文提出了一種新穎的方法,名為"具資料特徵位置感知的資料分布動態預測器(LA-DP)",旨在解決分類任務的真實世界概念漂移預測。LA-DP利用資料實例的特徵位置資訊與歷史資料分布趨勢來預測未來的資料分布。它通過採用編碼器-解碼器架構以及使用注意力機制來連接兩個模塊,展現出資料特徵位置感知的能力。我們還提出了一個框架,利用LA-DP預測的未來資料分布來生成未來的分類器。我們通過廣泛的實驗驗證了LA-DP相較於最先進方法的有效性。zh_TW
dc.description.abstractIn numerous real-world applications, streaming data is collected over time, and it is a common occurrence for the data distribution to undergo changes in nonstationary real-world environments. This phenomenon, referred to as concept drift, presents substantial challenges. Previous research has primarily concentrated on adapting models to the most recent concept to address concept drift. Additionally, there is often an opportunity to forecast the underlying factors driving concept drift and predict future concepts in many cases. Previous research has tackled concept drift prediction for regression tasks with real-world drift and classification tasks with incremental actual drift. However, research targeting real-world drift prediction for classification tasks is limited. In this paper, we propose the Location-aware Dynamic Predictor (LA-DP), a novel approach designed for real-world concept drift prediction for classification tasks. LA-DP leverages the location information and historical data distributions of data instances to predict future data distribution. It exhibits location-aware capability by incorporating an encoder-decoder architecture and employing the attention mechanism to establish connections between the two modules. We also develop a framework that utilizes LA-DP predictions to generate future classifiers. The effectiveness of LA-DP is demonstrated through extensive empirical experiments.en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-07T16:57:25Z
No. of bitstreams: 0
en
dc.description.provenanceMade available in DSpace on 2023-09-07T16:57:25Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontentsVerification Letter from the Oral Examination Committee i
Acknowledgements iii
摘要 iv
Abstract v
Contents vii
List of Figures ix
List of Tables xii
Chapter 1 Introduction 1
Chapter 2 Related Work 7
2.1 Concept Drift Adaptation 7
2.2 Concept Drift Prediction 9
Chapter 3 Problem Definition 13
Chapter 4 Methodology 16
4.1 Propsoed Method 16
4.2 Model Architecture 18
4.2.1 Encoder Module 19
4.2.2 Decoder Module 21
4.3 Optimization 21
4.4 LA-DP Framework 23
Chapter 5 Experiments 28
5.1 Datasets 28
5.2 Compared Methods 32
5.3 Hyperparameters 35
5.4 Quantitative Analysis 36
5.4.1 Comparison with Drift Prediction Methods 38
5.4.2 Comparison with Drift Adaptation Methods 39
5.4.3 Robustness Comparisons 40
5.5 Qualitative Analysis 40
5.5.1 Visualization of decision boundaries 41
5.5.2 Visualization of most attended timestamps 43
5.6 Hyperparameter Analysis 43
5.7 Generalizability Analysis 47
Chapter 6 Conclusion 49
References 50
-
dc.language.isoen-
dc.title使用具資料特徵位置感知的資料分布動態預測器進行概念漂移預測zh_TW
dc.titleConcept Drift Prediction with Location-aware Dynamic Predictoren
dc.typeThesis-
dc.date.schoolyear111-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee曹昱;彭文志;李政德zh_TW
dc.contributor.oralexamcommitteeYu Tsao;Wen-Chih Peng;Cheng-Te Lien
dc.subject.keyword概念漂移,概念漂移預測,資料流學習,深度學習,注意力機制,zh_TW
dc.subject.keywordConcept Drift,Concept Drift Prediction,Data Stream Learning,Deep Learning,Attention Mechanism,en
dc.relation.page53-
dc.identifier.doi10.6342/NTU202301790-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2023-08-07-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept資訊工程學系-
dc.date.embargo-lift2025-01-01-
顯示於系所單位:資訊工程學系

文件中的檔案:
檔案 大小格式 
ntu-111-2.pdf
授權僅限NTU校內IP使用(校園外請利用VPN校外連線服務)
6.09 MBAdobe PDF檢視/開啟
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved