| 基于PSO拓展的多分类器加权集成方法 |
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| 引用本文： | 刘擎超,朱玉全,陈耿.基于PSO拓展的多分类器加权集成方法[J].计算机工程,2012,38(7):174-176. |
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| 作者姓名： | 刘擎超 朱玉全 陈耿 |
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| 作者单位： | 1. 江苏大学计算机科学与通信工程学院,江苏镇江,212013
2. 南京审计学院信息科学学院,南京,211815 |
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| 基金项目： | 国家自然科学基金资助项目(70971067);江苏省自然科学基金资助项目(BK2010331) |
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| 摘 要： | 在多分类器集成时，每个基分类器的效能不同，如每个权值都相同，则会影响基分类器发挥作用。基于此，提出基于PSO拓展的多分类器加权集成方法BCPSO。该方法采用随机子空间生成各个独立的子分类器，输出结果通过各分类器加权投票组合规则集成。实验结果表明，该方法有效可行，具有较高的分类正确率。
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| 关 键 词： | 基分类器 加权投票 分类器 随机子空间 粒子群优化 |
| 收稿时间： | 2011-08-18 |
Multiple Classifiers Weighted Integration Method Based on Particle Swarm Optimization Expanded |
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| LIU Qing-chao
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ZHU Yu-quan
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CHEN Geng.Multiple Classifiers Weighted Integration Method Based on Particle Swarm Optimization Expanded[J].Computer Engineering,2012,38(7):174-176. |
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| Authors: | LIU Qing-chao ZHU Yu-quan CHEN Geng |
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| Affiliation: | 1.School of Computer Science and Telecommunications Engineering,Jiangsu University,Zhenjiang 212013,China 2.School of Information Science,Nanjing Audit University,Nanjing 211815,China) |
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| Abstract: | For the integration of multiple classifiers,each base classifier performance is different,if the weights are the same for each classifier,it is bound to affect the classification of base classifier.This paper proposes a weighting method Binary Crossover Particle Swarm Optimization(BCPSO),in which each individual sub-classifier uses random subspace method to generate and output the final classification by the combination of rule by weighted voting.Experimental results show that the method is feasible and effective,it has high classification accuracy. |
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| Keywords: | base classifier weighted voting classifier random subspace Particle Swarm Optimization(PSO) |
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