| 基于假设间隔的弱随机特征子空间生成算法 |
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| 引用本文： | 李志亮,黄丹.基于假设间隔的弱随机特征子空间生成算法[J].贵阳金筑大学学报,2012(3):1-10. |
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| 作者姓名： | 李志亮 黄丹 |
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| 作者单位： | [1]南京师范大学计算机科学与技术学院,江苏南京210046 [2]南京财经大学国际经济与贸易学院,江苏南京210046 |
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| 摘 要： | 集成算法是机器学习领域的研究热点。随机子空间算法是集成算法的一个主要算法。随机子空间生成的特征子集可能含有冗余特征、噪声特征,影响算法的分类精度。为此,本文提出了一种基于假设间隔的弱随机特征子空间生成算法（WRSSimba）,有效去除了特征子集中冗余特征和噪声特征。在UCI数据集上的实验结果表明,WRSSimba的分类性能优于随机子空间算法和Simba算法。
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| 关 键 词： | 集成学习 随机子空间 假设间隔 |
Weak Random subspace Based On Simba |
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| Li Zhiliang,Huang Dan.Weak Random subspace Based On Simba[J].Journal of Jinzhu University of Guiyang,2012(3):1-10. |
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| Authors: | Li Zhiliang Huang Dan |
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| Institution: | 2 ( 1. School of Computer Science and Technology, Nanjing Normal University, Nanjing 210046, China; 2. School of International Economics and Trade Institute, Nanjing University of Finance and Economics, Nanjing 210046, China) |
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| Abstract: | The ensemble algorithm is a hot research field of machine learning. Random subspace algorithm is a main algorithm of ensemble algorithm. Feature subset generated by random subspace may contain redundant feature and even noise feature, affecting the classification accuracy. Therefore, in this paper, Weak Random subspace Based On Simba （WRSSimba） algorithm is introduced. WRSSimba effectively eliminates the redundancy and noise feature of feature subspace. The experimental results on UCI datasets show that, WRSSimba classification performance is better than Random subspace algorithm. |
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| Keywords: | Ensemble Learning Random subspace Simba |
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