| Classifying Data Sets Using Support Vector Machines Based on Geometric Distance |
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| 作者姓名： | 王红梅 赵政 郑建华 |
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| 作者单位： | School of Electronic Information Engineering Tianjin University Tianjin 300072，China，School of Electronic Information Engineering Tianjin University，Tianjin 300072，China，School of Electronic Information Engineering Tianjin University，Tianjin 300072，China |
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| 摘 要： | Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule, and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.
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| 关 键 词： | 支撑向量机 数据挖掘 几何距离 分类精度 |
| 收稿时间： | 2005-11-17 |
Classifying Data Sets Using Support Vector Machines Based on Geometric Distance |
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| WANG Hongmei,ZHAO Zheng,ZHENG Jianhua.Classifying Data Sets Using Support Vector Machines Based on Geometric Distance[J].Transactions of Tianjin University,2006,12(2):153-156. |
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| Authors: | WANG Hongmei ZHAO Zheng ZHENG Jianhua |
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| Abstract: | Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule, and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy. |
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| Keywords: | support vector machines geometric distance classification accuracy |
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