English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 112881/143847 (78%)
Visitors : 50255264      Online Users : 546
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/152783
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152783


    Title: 利用孿生神經網路之特徵提取增強分類模型的表現
    Improving Classification Model Performance through Feature Extraction with Siamese Neural Network
    Authors: 沈冠宇
    Shen, Guan-Yu
    Contributors: 周珮婷
    沈冠宇
    Shen, Guan-Yu
    Keywords: 孿生神經網路
    特徵提取
    特徵學習
    維度縮減
    分類
    Siamese Neural Network
    Feature Extraction
    Feature Learning
    Dimensionality Reduction
    Classification
    Date: 2024
    Issue Date: 2024-08-05 14:00:50 (UTC+8)
    Abstract: 孿生神經網路是一種監督式的神經網路模型,用於學習有效衡量兩筆資料間的相似程度。其原理是將一對樣本同時輸入至兩個具有完全相同架構且共享權重的子網路中,並計算兩個子網路輸出向量間的相似度作為模型輸出,藉此判斷該樣本對為相似樣本或不相似樣本。本研究將孿生神經網路與分類任務進行結合,首先將資料輸入孿生神經網路進行訓練,並將其作為特徵提取器,提取子網路隱藏層的神經元輸出作為新特徵,再將這些新特徵放入目前最為熱門的分類演算法XGBoost中進行訓練,並以六組資料集進行驗證。結果顯示,利用提取的特徵進行模型訓練,其預測表現優於使用原始特徵之模型。此外,我們還嘗試先對提取出的特徵進行主成分分析後再進行訓練,以達成維度縮減的目的。結果顯示,在多數情況下,當累積解釋變異數比例達85%以上時,使用這些主成分進行訓練的預測表現比起使用原始特徵訓練來的更加優異。
    Siamese neural network is a supervised learning model that learns to measure the similarity between a pair of data. During model training, a pair of samples is simultaneously input into two subnetworks that have identical configuration and weights. Then the similarity between output vectors are calculated to determine whether the sample pair is similar or not. This study integrates Siamese neural network with classification tasks by serving Siamese network as a feature extractor. Output vectors from the hidden layers of the subnetworks are extracted as new features, which are then trained using the classification algorithm XGBoost on six datasets. The results indicate that models trained with extracted features outperform those trained with original features. Additionally, we implement Principal Component Analysis on the extracted features before training to achieve dimensionality reduction. The findings suggest that, in most cases, when the cumulative explained variance ratio is above 85%, the predictive performance using these principal components is superior to that using the original features.
    Reference: Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
    Ahlawat, S. and Choudhary, A. (2020). Hybrid cnn-svm classifier for handwritten digit recognition. Procedia Computer Science, 167:2554–2560. International Conference on
    Computational Intelligence and Data Science.
    Aydemir, G., Paynabar, K., and Acar, B. (2022). Robust feature learning for remaining useful life estimation using siamese neural networks. In 2022 30th European Signal
    Processing Conference (EUSIPCO), pages 1432–1436.
    Benkaddour, M. and Bounoua, A. (2017). Feature extraction and classification using deep convolutional neural networks, pca and svc for face recognition. Traitement du signal,
    34:77–91.
    Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
    Bromley, J., Bentz, J. W., Bottou, L., Guyon, I. M., LeCun, Y., Moore, C., Säckinger, E., and Shah, R. (1993). Signature verification using a ”siamese” time delay neural network. Int. J. Pattern Recognit. Artif. Intell., 7:669–688.
    Charytanowicz, M., Niewczas, J., Kulczycki, P., Kowalski, P., and Lukasik, S. (2012). Seeds. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5H30K.
    Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. ACM.
    Chopra, S., Hadsell, R., and LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference
    on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 539–546 vol.1.
    Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
    Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21–27.
    Durkota, K., Linda, M., Ludvik, M., and Tozicka, J. (2020). Neuron-net: Siamese network for anomaly detection. Technical report, DCASE2020 Challenge, Tech. Rep.
    Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189 – 1232.
    Hadsell, R., Chopra, S., and LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision
    and Pattern Recognition (CVPR’06), volume 2, pages 1735–1742.
    Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9:1735–80.
    Jmila, H., Ibn Khedher, M., Blanc, G., and El Yacoubi, M. A. (2019). Siamese network based feature learning for improved intrusion detection. In Gedeon, T., Wong, K. W., and Lee, M., editors, Neural Information Processing, pages 377–389, Cham. Springer International Publishing.
    Koch, G., Zemel, R., Salakhutdinov, R., et al. (2015). Siamese neural networks for oneshot image recognition. In ICML deep learning workshop, volume 2.
    Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266):1332–1338.
    LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition.
    Neural Computation, 1(4):541–551.
    Moustakidis, S. and Karlsson, P. (2020). A novel feature extraction methodology using siamese convolutional neural networks for intrusion detection. Cybersecurity, 3.
    Mowforth, P. and Shepherd, B. (1992). Statlog (Vehicle Silhouettes). UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5HG6N.
    O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al. (2019). Kerastuner. https://github.com/keras-team/keras-tuner.
    Ozkan, I. A., Koklu, M., and Saraçoğlu, R. (2021). Classification of pistachio species using improved k-nn classifier. Progress in Nutrition, 23:e2021044.
    Pearson, K. (1901). Liii. on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science,
    2(11):559–572.
    Rumelhart, D. E. and McClelland, J. L. (1987). Learning Internal Representations by Error Propagation, pages 318–362. MIT Press.
    Sengupta, D., Ali, S. N., Bhattacharya, A., Mustafi, J., Mukhopadhyay, A., and Sengupta, K. (2022). A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer
    based on nuclear morphology. PLOS ONE, 17(1):1–20.
    Siegler, R. (1994). Balance Scale. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5488X.
    Tsalera, E., Papadakis, A., Samarakou, M., and Voyiatzis, I. (2022). Feature extraction with handcrafted methods and convolutional neural networks for facial emotion recognition. Applied Sciences, 12:8455.
    Description: 碩士
    國立政治大學
    統計學系
    111354031
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111354031
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    403101.pdf1334KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback