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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復(Cheng-Fu Chou) | |
| dc.contributor.author | Wai-Man Wong | en |
| dc.contributor.author | 黃慧敏 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:07:01Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-23T09:07:01Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-08 | |
| dc.identifier.citation | T. Alam, C. F. Ahmed, S. A. Zahin, M. A. H. Khan, and M. T. Islam. An effective ensemble method for multiclass classification and regression for imbalanced data. Advances in Data Mining. Applications and Theoretical Aspects Lecture Notes in Computer Science, page 59–74, 2018. S. Barua, M. M. Islam, X. Yao, and K. Murase. Mwmote–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on Knowledge and Data Engineering, 26(2):405–425, 2014. C. L. Castro and A. P. Braga. Novel costsensitive approach to improve the multilayer perceptron performance on imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 24(6):888–899, 2013 A. Fernández, M. J. D. Jesus, and F. Herrera. Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced datasets. International Journal of Approximate Reasoning, 50(3):561–577, 2009. N. N. Haroon, P. C. Austin, B. R. Shah, J. Wu, S. S. Gill, and G. L. Booth. Risk of dementia in seniors with newly diagnosed diabetes: A populationbased study. Diabetes Care, 38(10):1868–1875, 2015. T. Hastie, R. Tibshirani, and J. H. Friedman. The elements of statistical learning data mining, inference, and prediction. Springer, 2009. H. He and E. Garcia. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263–1284, 2009. M. Lind, L. A. GarciaRodriguez, G. L. Booth, L. CeaSoriano, B. R. Shah, G. Ekeroth, and L. L. Lipscombe. Mortality trends in patients with and without diabetes in ontario, canada and the uk from 1996 to 2009: a populationbased study, Oct 2013. L. L. Lipscombe and J. E. Hux. Trends in diabetes prevalence, incidence, and mortality in ontario, canada 1995–2005: a populationbased study. The Lancet, 369(9563):750–756, 2007. X.Y. Liu, J. Wu, and Z.H. Zhou. Exploratory undersampling for classimbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(2):539–550, 2009. D. Mease, V. Profile, A. J. Wyner, A. Buja, C. M. A. D. M. G. L. P. Y. P. counts8Available for Download7Citation count136Downloads (cumulative)2, and D. M. G. L. P. Y. P. counts8Available for Download7Citation count136Downloads (cumulative)2. Boosted classification trees and class probability/quantile estimation, May 2007. N. Nikolaou, N. Edakunni, M. Kull, P. Flach, and G. Brown. Costsensitive boosting algorithms: Do we really need them? Machine Learning, 104(23):359–384, 2016. C. Seiffert, T. M. Khoshgoftaar, J. V. Hulse, and A. Napolitano. Rusboost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 40(1):185–197, 2010. B. Strack, J. P. Deshazo, C. Gennings, J. L. Olmo, S. Ventura, K. J. Cios, and J. N. Clore. Impact of hba1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records. BioMed Research International, 2014:1– 11, 2014. K. M. Ting. A comparative study of costsensitive boosting algorithms. In In Proceedings of the 17th International Conference on Machine Learning, pages 983– 990. Morgan Kaufmann. J. Vanschoren, J. N. V. Rijn, B. Bischl, and L. Torgo. Openml. ACM SIGKDD Explorations Newsletter, 15(2):49–60, 2014. N. Wang, X. Zhao, Y. Jiang, and Y. Gao. Iterative metric learning for imbalance data classification. Proceedings of the TwentySeventh International Joint Conference on Artificial Intelligence, 2018. K. Q. Weinberger and G. Tesauro. Metric learning for kernel regression, Mar 2007. S. Wold, K. Esbensen, and P. Geladi. Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1):37–52, 1987. Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists. J. Yin, C. Gan, K. Zhao, X. Lin, Z. Quan, and Z.J. Wang. A novel model for imbalanced data classification. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):6680–6687, 2020 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79670 | - |
| dc.description.abstract | 近年來，糖尿病的發病率和患病率顯著增加。根據美國糖尿病協會進行的基於人群的研究，與沒有糖尿病的人相比，患有糖尿病的人患失智症的發生率略高。 隨著世界人口老齡化，糖尿病和失智症預計將成為全球流行病。 本文的目的是糖尿病患者之失智症預測。由於大約有2.68\%的糖尿病患者患有失智症，因此我們提出了一種基於深度神經網絡的模型，即使在高不平衡比率的數據集中，該模型也能夠準確預測失智症患者。 為了評估所提出模型的性能，我們同時在 8 個不平衡的公開數據集上進行了實驗。 結果表明，我們的模型在召回率、G 均值和 AUC 方面優於目前最先進的方法。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:07:01Z (GMT). No. of bitstreams: 1 U0001-3108202110410900.pdf: 1670986 bytes, checksum: 6a357e25400d24008d6cdf3b8d04df79 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . i 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . .iii Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . v Contents . . . . . . . . . . . . . . . . . . . . . . . . . . .vii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . .ix List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . .xi Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Sampling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Costsensitive methods . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Ensemble Learning Methods . . . . . . . . . . . . . . . . . . . . . . 5 2.4 DDAE Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 3 Problem clarification and Dataset 7 3.1 Problem clarification . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Diabetes Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 4 Method 11 4.1 Data Preprocess for Diabetes Dataset . . . . . . . . . . . . . . . . . 11 4.2 Proposed Model Architecture . . . . . . . . . . . . . . . . . . . . . 15 4.2.1 Data Block Construction (DBC) component . . . . . . . . . . . . . 15 4.2.2 Dimensionality Reduction (DR) Component . . . . . . . . . . . . . 17 4.2.3 Ensemble Learning (EL) component . . . . . . . . . . . . . . . . . 19 Chapter 5 Result and Evaluation 21 5.1 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.2 Experimental setup for Diabetes Dataset and its Result . . . . . . . . 22 5.3 Other Dataset Result . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.4 Ablation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 6 Conclusion 29 References 31 Appendix A — List of features and their descriptions in the Diabetes Dataset 35 Appendix B — MLKR Demensionality Comparison 39 | |
| dc.language.iso | en | |
| dc.title | 以深度神經網絡為基礎預測糖尿病患者失智症之機率 | zh_TW |
| dc.title | Dementia Prediction among Diabetes Patients based on Deep Neural Network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳曉光(Hsin-Tsai Liu),林澤(Chih-Yang Tseng),李明穗,蘇東弘 | |
| dc.subject.keyword | 機器學習,神經網絡,不平衡資料,二元分類, | zh_TW |
| dc.subject.keyword | Machine learning,Neural Network,Imbalanced Data,Binary Classification, | en |
| dc.relation.page | 40 | |
| dc.identifier.doi | 10.6342/NTU202102877 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-10-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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