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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林軒田(Hsuan-Tien Lin) | |
dc.contributor.author | TeKang Jan | en |
dc.contributor.author | 詹德剛 | zh_TW |
dc.date.accessioned | 2021-06-15T05:44:38Z | - |
dc.date.available | 2011-08-20 | |
dc.date.copyright | 2010-08-20 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-19 | |
dc.identifier.citation | Naoki Abe, Bianca Zadrozny, and John Langford. An iterative method for multi-class
cost-sensitive learning. In Won Kim, Ron Kohavi, Johannes Gehrke, and William Du- Mouchel, editors, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3–11. ACM, 2004. Alina Beygelzimer, John Langford, and Pradeep Ravikumar. Multiclass classification with filter trees. Downloaded from http://hunch.net/˜jl, 2007. Leon Bottou, Corinna Cortes, John S. Denker, Harris Drucker, Isabelle Guyon, L.D Jackel, Yann LeCun, Urs A. Muller, Eduard Sackinger, Patrice Simard, and Vladimir Vapnik. Comparison of classifier methods: a case study in handwritten digit recognition. In Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision Image Processing., Proceedings of the 12th IAPR International. Conference on, volume 2, pages 77 –82 vol.2, 9-13 1994. doi: 10.1109/ICPR.1994.576879. Christopher J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2:121–167, 1998. Koby Crammer and Yoram Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, pages 265–292, 2001. Pedro Domingos. MetaCost: A general method for making classifiers cost-sensitive. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 155–164. ACM SIGKDD, ACM, 1999. 33 Charles Elkan. The foundations of cost-sensitive learning. In International Joint Conference on Artificial Intelligence, volume 17, pages 973–978, 2001. Seth Hettich, Catherine L. Blake, and Christopher J. Merz. UCI repository of machine learning databases, 1998. Downloadable at http://www.ics.uci.edu/ ˜mlearn/MLRepository.html. Chih-Wei Hsu and Chih-Jen Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415–425, 2002. Ulrich H.-G. Kressel. Pairwise classification and support vector machines. pages 255– 268, 1999. John Langford and Alina Beygelzimer. Sensitive error correcting output codes. In Peter Auer and Ron Meir, editors, Learning Theory: 18th Annual Conference on Learning Theory, volume 3559, pages 158–172. Springer-Verlag, 2005. Chun-Fu Lin and Sheng-De Wang. Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13(2):464–471, 2002. Hsuan-Tien Lin. From Ordinal Ranking to Binary Classification. PhD thesis, California Institute of Technology, 2008. Edgar Osuna, Robert Freund, and Federico Girosi. Training support vector machines: an application to face detection. pages 130–136, 1997. Bernhard Schlkopf, B. Scholkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi, and V. Vapnik. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45:2758–2765, 1997. Han-Hsing Tu and Hsuan-Tien Lin. One-sided support vector regression for multiclass cost-sensitive classification. In Machine Learning: Proceedings of the 27th International Conference, pages 1095–1102. ACM, 2010. Vladimir N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998. 34 Bianca Zadrozny, John Langford, and Naoki Abe. Cost sensitive learning by costproportionate example weighting. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003). IEEE Computer Society, 2003. Zhi-Hua Zhou and Xu-Ying Liu. On multi-class cost-sensitive learning. In In Proceeding of the 21st National Conference on Artificial Intelligence, volume 21, pages 567–572. Boston, WA, 2006. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46984 | - |
dc.description.abstract | 成本導向多重分類問題的應用不僅可見於許多方面,像是文件分類或是生物研究等,而且也是一個很熱門的研究領域。在這個問題中,為了使做出來的分類決策達到最低成本,有許多演算法被提出,然而至今卻沒有一個公平的平台去比較他們。而我們提供公正的環境,比較數種成本導向多重分類的演算法,配合支持向量機,並加以討論這些演算法的效能。除此之外,我們延伸了一個演算法到成本導向多重分類問題,並且推導出他的損失上限。我們的實驗指出,在成本導向多重分類問題中,這個延伸的演算法不僅比原來的演算法優越,而且也比一些成本導向多重分類問題的演算法好。 | zh_TW |
dc.description.abstract | Multi-class cost-sensitive classification problem, which treats different cost for different types of misclassification, is currently attracting much research
attention. Several promising meta-algorithms have been developed for this problem. Nevertheless, an empirical comparison of these algorithms has not been conducted. We would give a fair setup to compare them. In this thesis, we focus on the support vector machine (SVM) framework to solve the multi-class cost-sensitive classification problem. SVM is one of the most popular tools used in classification. In addition, we extend the all-together method in SVM formulation to the field of cost-sensitive problem. We also derive the theoretical guarantee of the extension. The experiment indicates this extension can get better performance than some existing regular classification algorithm and cost-sensitive classification algorithm in many cost-sensitive scenarios. The result also shows 'cost-sensitive one-versus-one','cost-sensitive one-sided regression' and this extension are more suitable than other cost-sensitive classification algorithms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T05:44:38Z (GMT). No. of bitstreams: 1 ntu-99-R97922090-1.pdf: 745478 bytes, checksum: 5e7f4a7b0ab654432d5218c9b4d21048 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 致謝i
中文摘要iii Abstract v 1 Introduction 1 1.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Basic Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.2 Weighted Support Vector Machine . . . . . . . . . . . . . . . . . 5 1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Existing Cost-sensitive Classification Algorithms 7 2.1 Multi-Class Support Vector Machine . . . . . . . . . . . . . . . . . . . . 7 2.2 Cost-Sensitive One-Versus-One . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Cost-Sensitive One-Versus-All . . . . . . . . . . . . . . . . . . . . . . . 10 2.4 Cost-Sensitive Filter Tree . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 A New Method by Considering All Data at Once 13 3.1 Crammer and Singer SVM . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Cost-Sensitive Crammer and Singer SVM . . . . . . . . . . . . . . . . . 16 3.3 Cost-Sensitive Crammer and Singer SVM Error Bound . . . . . . . . . . 18 4 Experiments and Discussions 21 4.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.1 Date Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.2 Parameter Selection . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1.3 Package Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 First Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.4 Second Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.5 Third Cost Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5 Conclusion 31 Bibliography 33 | |
dc.language.iso | en | |
dc.title | 各式成本導向支持向量機之比較 | zh_TW |
dc.title | A Comparison of Methods for Cost-sensitive Support Vector Machines | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李育杰(YUH-JYE LEE),徐宏民,王鈺強 | |
dc.subject.keyword | 成本導向多重分類,成本資訊,支持向量機,多類支持向量機, | zh_TW |
dc.subject.keyword | multi-class cost-sensitive classification,cost information,support vector machines,multi-class support vector machines, | en |
dc.relation.page | 35 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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