| 基于离散化方法的支持向量机集成研究 |
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| 引用本文： | 蔡铁,伍星,李烨.基于离散化方法的支持向量机集成研究[J].现代计算机,2008(7). |
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| 作者姓名： | 蔡铁 伍星 李烨 |
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| 作者单位： | 深圳信息职业技术学院信息技术研究所,深圳518029 |
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| 基金项目： | 国家自然科学基金，广东省深圳市科技计划 |
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| 摘 要： | 基于数据离散化方法,提出一种新的支持向量机集成算法,该算法采用粗糙集和布尔推理离散化方法构造有差异的基分类器.并引入一致度指标控制离散化过程,可进一步提高集成学习的分类性能.实验结果表明,该算法不仅具有明显优于单一支持向量机的分类性能.而且能取得比传统集成学习算法Bagging和Adaboost更高的分类正确率.
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| 关 键 词： | 支持向量机(SVM) 支持向量机集成 离散化 粗糙集与布尔推理(RSBRA) |
Research on SVM Ensemble Based on Discretization Method |
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| CAI Tie,WU Xing,LI Ye.Research on SVM Ensemble Based on Discretization Method[J].Modem Computer,2008(7). |
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| Authors: | CAI Tie WU Xing LI Ye |
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| Affiliation: | CAI Tie,WU Xing,LI Ye (Institute of Information Technology,Shenzhen Institute of Information Technology,Shenzhen 518029) |
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| Abstract: | Proposes a novel Support Vector Machine(SVM) ensemble algorithm based on discretization method, this algorithm uses the rough sets and boolean reasoning approach, which is controlled by the consistency level coined from the rough sets theory, to construct base classifiers with good diversity so that the performance of ensemble learning can be improved. Experimental results show that the proposed algorithm has better classification performance than single SVM. Compared with the traditional ensemble learning ... |
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| Keywords: | Support Vector Machine(SVM) SVM Ensemble Discretization Rough Set and Boolean Reasoning Approach(RSBRA) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
