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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46984
標題: | 各式成本導向支持向量機之比較 A Comparison of Methods for Cost-sensitive Support Vector Machines |
作者: | TeKang Jan 詹德剛 |
指導教授: | 林軒田(Hsuan-Tien Lin) |
關鍵字: | 成本導向多重分類,成本資訊,支持向量機,多類支持向量機, multi-class cost-sensitive classification,cost information,support vector machines,multi-class support vector machines, |
出版年 : | 2010 |
學位: | 碩士 |
摘要: | 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。 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46984 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊工程學系 |
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