林軒田Lin, Hsuan-Tien臺灣大學:資訊工程學研究所涂漢興Tu, Han-HsingHan-HsingTu2010-05-172018-07-052010-05-172018-07-052009U0001-1908200916263600http://ntur.lib.ntu.edu.tw//handle/246246/183402成本導向多重分類問題在近年來越來越為重要，在圖形辨識和醫學究等問題上有很高的應用價值。為了使做出的分類決策可以達到最成本，陸續有許多研究者提出了加入成本資訊的機器學習演算法。些演算法中最常見的步驟，是將成本資訊轉化為每筆資料的比重。本篇論文中，我們採取了一項不同的步驟：利用迴歸分析來預估每資料相對應的分類成本，並依最低的預估成本來做分類決策。此方簡單並可以和各式的迴歸分析演算法結合，並有很強的理論基礎支。我們更進一步的分析了前述方法的盲點，利用創新的迴歸損失函，配合支持向量機，來設計更強而有力的成本導向多重分類演算，並透過實驗展示了本演算法的優越性。Cost-sensitive classification is an important research problem in recentears. It allows machine learning algorithms to use the additional cost informationo make more strategic decisions.tudies on binary cost-sensitive classification have led to promising resultsn theories, algorithms, and applications. The multi-class counterpart islso needed in many real-world applications, but is more difficult to analyze.his thesis focuses on multi-class cost-sensitive classification.xisting methods for multi-class cost-sensitive classification usually transformhe cost information into example importance (weight). This thesis offers different viewpoint of the problem, and proposes a novel method. Weirectly estimate the cost value corresponding to each prediction using regression,nd outputs the label that comes with the smallest estimated cost.e improve the method by analyzing the errors made during the decision.hen, we propose a different regression loss function that tightly connectsith the errors. The new loss function leads to a solid theoretical guaranteef error transformation. We design a concrete algorithm for the loss functionith the support vector machines. The algorithm can be viewed as a theoreticallyustified extension the popular one-versus-all support vector machine.xperiments using real-world data sets with arbitrary cost values demonstratehe usefulness of our proposed methods, and validate that the cost informationhould be appropriately used instead of dropped.致謝i文摘要iiibstract v Introduction 1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Existing Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Cost-Sensitive Classification by Per Class Regression 9.1 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Regression Error Bound . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Cost-Sensitive Classification by One-Sided Regression 19.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 One-sided Support Vector Regression . . . . . . . . . . . . . . . . . . . 24.2.1 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . 26.2.2 Comparison with Artificial Data Set . . . . . . . . . . . . . . . . 27.2.3 Comparison with Benchmark Data Sets . . . . . . . . . . . . . . 28 Conclusion 31ibliography 33application/pdf956888 bytesapplication/pdfen-US成本導向多重分類成本資訊迴歸分析支持向量機multi-class cost-sensitive classificationcost informationregressionsupport vector machinesthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/183402/1/ntu-98-R96922139-1.pdf