| 一种非平衡分布数据的支持向量机新算法 |
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| 引用本文： | 孙蕾,周明全,李丙春.一种非平衡分布数据的支持向量机新算法[J].计算机应用,2004,24(12):14-15. |
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| 作者姓名： | 孙蕾 周明全 李丙春 |
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| 作者单位： | 西北大学,计算机科学系,陕西,西安,710069 |
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| 基金项目： | 国家自然科学基金资助项目 (6 0 3 72 0 72 ) |
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| 摘 要： | 支持向量机是近几年发展起来的机器学习方法，它是利用接近边界的少数向量来构造一个最优分类面。然而当两类中的样本数量差别悬殊时，支持向量机的分类能力会下降。为了解决此问题，文中提出了一种改进的支持向量机算法——DFP-SVM算法。实验表明，此方法在解决两类样本数量十分不均衡问题时有着很强的分类能力。
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| 关 键 词： | DFP-SVM 支持向量机 不均衡数据分类 |
| 文章编号： | 1001-9081(2004)12-0014-02 |
Novel algorithm of SVM for unbalanced data |
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| SUN Lei,ZHOU Ming-quan,LI Bing-chun.Novel algorithm of SVM for unbalanced data[J].journal of Computer Applications,2004,24(12):14-15. |
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| Authors: | SUN Lei ZHOU Ming-quan LI Bing-chun |
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| Abstract: | Support vector machine is an algorithm of machine learning that has developed during these years. It constructs an optimal hyperplane utilizing a small set of vectors near boundary. However, when the two-class problem samples are very unbalanced, SVM has a poor performance. An improved SVM: DFP-SVM was presented here. Computational results indicate that the modified algorithm has a strong capability of classification for the unbalanced samples of the two-class problems. |
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| Keywords: | DFP-SVM support vector machine unbalanced data classification |
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