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題名 優化資料清理與機器學習的機制
The refined mechanism for data cleaning and machine learning
作者 余艾玨
Yu, Ai-Chueh
貢獻者 蔡瑞煌
Tsaih, Rua-Huan
余艾玨
Yu, Ai-Chueh
關鍵詞 人工神經網路
正規化
單一隱藏層倒傳遞神經網路
Artificial neural networks
Regularization
Single-hidden layer feed-forward neural networks
Resistant learning with envelope module
日期 2018
上傳時間 2-八月-2018 16:15:32 (UTC+8)
摘要 近年來人工智慧在機器學習的應用扮演重要的角色，而相較於大數據分析的統計方法，ANN成為最有用方法中的其中一個，為了處理動態環境中的時間序列資料和離群值，Wu (2017)提出一個資料清理和機器學習的機制，實驗結果顯示提出的機制在資料清理和機器學習方面是很有效的，Wu (2017)已經透過單一隱藏層倒傳遞神經網路實作RLEM，這個研究將使用兩個方法優化此機制，一個是在RLEM的損失函數(loss function)加上正規化項來避免過度擬合(overfitting)的問題，另一個是修改RLEM並透過新版的Tensorflow實作來達成目標。
In recent years, artificial intelligence (AI) has become an important part in the application of machine learning, and the artificial neural networks (ANN) serves as one of the most useful methods compared to statistical methods for the purpose of big data analytics. To cope with the time series data that may have concept-drifting phenomenon and outliers, Wu (2017) had derived a mechanism for effective data cleaning and machine learning. The experiment results had shown that the proposed mechanism is promising in effective data cleaning and machine learning. Wu (2017) had implemented the resistant learning with envelope module (RLEM) via the adaptive single-hidden layer feed-forward neural networks (SLFN). This research will add the regularization term to loss function to prevent overfitting and will refine RLEM to improve the accuracy of the predicted return of carry trade. The refined mechanism will be implemented via the updated version of Tensorflow.
參考文獻 1. Android Authority (2018) “Artificial intelligence vs machine learning : what’s the difference?”, available at https://www.androidauthority.com/artificial-intelligence-vs-machine-learning-832331/ (accessed 5 March 2018)
     2. J. Cao, Y. Pang, X. Li, J. Liang (2018) “Randomly translational activation inspired by the input distributions of ReLU,” Neurocomputing (275), pp:859-868
     3. D.A. Clevert, T. Unterthiner, S. Hochreiter (2016) “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs),” Published as a conference paper at ICLR
     4. Educational Research Techniques (2016) “Black Box Method-Artificial Neural Networks”, available at
     https://educationalresearchtechniques.com/2016/07/06/black-box-method-artificial-neural-networks/ (accessed 5 March 2018)
     5. Enhance Data Science (2017) “Machine Learning Explained: Regularization”, available at
     http://enhancedatascience.com/2017/07/04/machine-learning-explained-regularization/ (accessed 5 March 2018)
     6. I. Goodfellow , Y. Bengio, A. Courville (2016), “Deep Learning,” The MIT Press
     7. S. Y. Huang, J. W. Lin, and R. H. Tsaih (2106), “Outlier Detection in the Concept Drifting Environment,” In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp:31-37
     8. S. Y. Huang, F. Yu, R. H. Tsaih, and Y. Huang (2104), “Resistant learning on the envelope bulk for identifying anomalous patterns,” In: Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), pp:3303-3310
     9. Investopedia “Carry Tradde” available at https://www.investopedia.com/terms/c/carry-trade.asp-0 (accessed 20 March 2018)
     10. Ò. Jordà, and A. M. Taylor (2012), “The carry trade and fundamentals: Nothing to fear but FEER itself,” Journal of International Economics, vol. 88, pp:74-90
     11. F. F. Li, J. Johnson, S. Yeung (2017), “Convolutional Neural Networks for Visual Recognition, Stanford University School of Engineering,” available at http://cs231n.stanford.edu/ (accessed 5 March 2018)
     12. J. D. Olden, M. K. Joy, R. G. Death (2004), “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data,” Ecological Modeling (178:3), pp:389-397
     13. Quora (2013), “Differences between L1 and L2 as Loss Function and Regularization”, available at
     http://www.chioka.in/differences-between-l1-and-l2-as-loss-function-and-regularization/ (accessed 5 March 2018)
     14. S. Ruder (2016), “An overview of gradient descent optimization algorithms”, available at http://ruder.io/optimizing-gradient-descent/index.html#adam (accessed 5 March 2018)
     15. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov (2014) “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research (15) 2014, pp:1929-1958
     16. The Theory of Everything (2017), “Understanding Activation Functions in Neural Networks”, available at https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0 (accessed 5 March 2018).
     17. Towards Data Science (2017), “Types of Optimization Algorithms used in Neural Networks and Ways to Optimize Gradient Descent”, available at https://towardsdatascience.com/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f (accessed 5 March 2018).
     18. R. H. Tsaih, T. C. Cheng (2009), “A resistant learning procedure for coping with outliers,” Annals of Mathematics and Artificial Intelligence (57:2), pp:161-180
     19. J. V. Tu (1996), “Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes” Journal of Clinical Epidemiology 49(11), pp:1225-1231.
     20. F. Y. Tzeng, K. L. Ma (2005), “Opening the Black Box — Data Driven Visualization of Neural Networks”, Visualization, IEEE
     21. L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, R. Fergus (2013), “Regularization of Neural Networks using DropConnect” Proceedings of the 30th International Conference on Machine Learning, PMLR (28:3), pp:1058-1066
     22. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma (2009), “Robust face recognition via sparse representation,” IEEE Transactions (31:1), pp:210-227
     23. J. Wu. (2017), “Application of Machine Learning to Predicting the Returns of Carry Trade. Unpubliched Master Thesis,” National Chengchi University, Taipei
     24. S. N. Zeng, J. P. Gou, L. M. Deng (2017), “An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization,” Expert Systems with Applications (82), pp:1-9
描述 碩士
國立政治大學
資訊管理學系
105356017
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356017
資料類型 thesis
dc.contributor.advisor 蔡瑞煌zh_TW
dc.contributor.advisor Tsaih, Rua-Huanen_US
dc.contributor.author (作者) 余艾玨zh_TW
dc.contributor.author (作者) Yu, Ai-Chuehen_US
dc.creator (作者) 余艾玨zh_TW
dc.creator (作者) Yu, Ai-Chuehen_US
dc.date (日期) 2018en_US
dc.date.accessioned 2-八月-2018 16:15:32 (UTC+8)-
dc.date.available 2-八月-2018 16:15:32 (UTC+8)-
dc.date.issued (上傳時間) 2-八月-2018 16:15:32 (UTC+8)-
dc.identifier (其他 識別碼) G0105356017en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119155-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 105356017zh_TW
dc.description.abstract (摘要) 近年來人工智慧在機器學習的應用扮演重要的角色，而相較於大數據分析的統計方法，ANN成為最有用方法中的其中一個，為了處理動態環境中的時間序列資料和離群值，Wu (2017)提出一個資料清理和機器學習的機制，實驗結果顯示提出的機制在資料清理和機器學習方面是很有效的，Wu (2017)已經透過單一隱藏層倒傳遞神經網路實作RLEM，這個研究將使用兩個方法優化此機制，一個是在RLEM的損失函數(loss function)加上正規化項來避免過度擬合(overfitting)的問題，另一個是修改RLEM並透過新版的Tensorflow實作來達成目標。zh_TW
dc.description.abstract (摘要) In recent years, artificial intelligence (AI) has become an important part in the application of machine learning, and the artificial neural networks (ANN) serves as one of the most useful methods compared to statistical methods for the purpose of big data analytics. To cope with the time series data that may have concept-drifting phenomenon and outliers, Wu (2017) had derived a mechanism for effective data cleaning and machine learning. The experiment results had shown that the proposed mechanism is promising in effective data cleaning and machine learning. Wu (2017) had implemented the resistant learning with envelope module (RLEM) via the adaptive single-hidden layer feed-forward neural networks (SLFN). This research will add the regularization term to loss function to prevent overfitting and will refine RLEM to improve the accuracy of the predicted return of carry trade. The refined mechanism will be implemented via the updated version of Tensorflow.en_US
dc.description.tableofcontents Abstract 3
     Figure Index 5
     Table Index 6
     1 Introduction 7
     1.1 Background 7
     1.2 Motivation 8
     1.3 Objective 9
     2 Literature Review 10
     2.1 Regularization 10
     2.2 Gradient descent optimization algorithms 11
     Backpropagation 12
     2.3 GPU and Tensorflow 14
     2.4 The resistant learning with envelope module 15
     2.5 The mechanism for data cleaning and machine learning 19
     3 Experiment Design 24
     3.1 Data description 24
     3.2 Experiment design 25
     4 Experiment results 29
     5 Conclusion and future work 33
     Reference 35		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356017en_US
dc.subject (關鍵詞) 人工神經網路zh_TW
dc.subject (關鍵詞) 正規化zh_TW
dc.subject (關鍵詞) 單一隱藏層倒傳遞神經網路zh_TW
dc.subject (關鍵詞) Artificial neural networksen_US
dc.subject (關鍵詞) Regularizationen_US
dc.subject (關鍵詞) Single-hidden layer feed-forward neural networksen_US
dc.subject (關鍵詞) Resistant learning with envelope moduleen_US
dc.title (題名) 優化資料清理與機器學習的機制zh_TW
dc.title (題名) The refined mechanism for data cleaning and machine learningen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Android Authority (2018) “Artificial intelligence vs machine learning : what’s the difference?”, available at https://www.androidauthority.com/artificial-intelligence-vs-machine-learning-832331/ (accessed 5 March 2018)
     2. J. Cao, Y. Pang, X. Li, J. Liang (2018) “Randomly translational activation inspired by the input distributions of ReLU,” Neurocomputing (275), pp:859-868
     3. D.A. Clevert, T. Unterthiner, S. Hochreiter (2016) “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs),” Published as a conference paper at ICLR
     4. Educational Research Techniques (2016) “Black Box Method-Artificial Neural Networks”, available at
     https://educationalresearchtechniques.com/2016/07/06/black-box-method-artificial-neural-networks/ (accessed 5 March 2018)
     5. Enhance Data Science (2017) “Machine Learning Explained: Regularization”, available at
     http://enhancedatascience.com/2017/07/04/machine-learning-explained-regularization/ (accessed 5 March 2018)
     6. I. Goodfellow , Y. Bengio, A. Courville (2016), “Deep Learning,” The MIT Press
     7. S. Y. Huang, J. W. Lin, and R. H. Tsaih (2106), “Outlier Detection in the Concept Drifting Environment,” In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp:31-37
     8. S. Y. Huang, F. Yu, R. H. Tsaih, and Y. Huang (2104), “Resistant learning on the envelope bulk for identifying anomalous patterns,” In: Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), pp:3303-3310
     9. Investopedia “Carry Tradde” available at https://www.investopedia.com/terms/c/carry-trade.asp-0 (accessed 20 March 2018)
     10. Ò. Jordà, and A. M. Taylor (2012), “The carry trade and fundamentals: Nothing to fear but FEER itself,” Journal of International Economics, vol. 88, pp:74-90
     11. F. F. Li, J. Johnson, S. Yeung (2017), “Convolutional Neural Networks for Visual Recognition, Stanford University School of Engineering,” available at http://cs231n.stanford.edu/ (accessed 5 March 2018)
     12. J. D. Olden, M. K. Joy, R. G. Death (2004), “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data,” Ecological Modeling (178:3), pp:389-397
     13. Quora (2013), “Differences between L1 and L2 as Loss Function and Regularization”, available at
     http://www.chioka.in/differences-between-l1-and-l2-as-loss-function-and-regularization/ (accessed 5 March 2018)
     14. S. Ruder (2016), “An overview of gradient descent optimization algorithms”, available at http://ruder.io/optimizing-gradient-descent/index.html#adam (accessed 5 March 2018)
     15. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov (2014) “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research (15) 2014, pp:1929-1958
     16. The Theory of Everything (2017), “Understanding Activation Functions in Neural Networks”, available at https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0 (accessed 5 March 2018).
     17. Towards Data Science (2017), “Types of Optimization Algorithms used in Neural Networks and Ways to Optimize Gradient Descent”, available at https://towardsdatascience.com/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f (accessed 5 March 2018).
     18. R. H. Tsaih, T. C. Cheng (2009), “A resistant learning procedure for coping with outliers,” Annals of Mathematics and Artificial Intelligence (57:2), pp:161-180
     19. J. V. Tu (1996), “Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes” Journal of Clinical Epidemiology 49(11), pp:1225-1231.
     20. F. Y. Tzeng, K. L. Ma (2005), “Opening the Black Box — Data Driven Visualization of Neural Networks”, Visualization, IEEE
     21. L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, R. Fergus (2013), “Regularization of Neural Networks using DropConnect” Proceedings of the 30th International Conference on Machine Learning, PMLR (28:3), pp:1058-1066
     22. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma (2009), “Robust face recognition via sparse representation,” IEEE Transactions (31:1), pp:210-227
     23. J. Wu. (2017), “Application of Machine Learning to Predicting the Returns of Carry Trade. Unpubliched Master Thesis,” National Chengchi University, Taipei
     24. S. N. Zeng, J. P. Gou, L. M. Deng (2017), “An antinoise sparse representation method for robust face recognition via joint l1 and l2 regularization,” Expert Systems with Applications (82), pp:1-9		zh_TW
dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.011.2018.A05-