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| 基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法 | |
| 引用本文： | 彭鹏,倪志伟,朱旭辉,夏平凡. 基于改进二元萤火虫群优化算法和邻域粗糙集的属性约简方法[J]. 模式识别与人工智能, 2020, 33(2): 95-105. DOI: 10.16451/j.cnki.issn1003-6059.202002001 |
| 作者姓名： | 彭鹏 倪志伟 朱旭辉 夏平凡 |
| 作者单位： | 1. 合肥工业大学 管理学院 合肥 230009; 2. 北方民族大学 银川 750021; 3. 合肥工业大学 过程优化与智能决策教育部重点实验室 合肥 230009 |
| 基金项目： | 国家自然科学基金项目(No.71490725,71521001,91546108);国家自然科学基金青年项目(No.71701061);安徽省自然科学基金项目(No.1908085QG298);中央高校基本科研业务费专项资金项目(No.JZ2019HGTA0053,JZ2019HGBZ0128)资助。 |
| 摘 要： | 针对数据降维和去冗问题,提出基于改进的二元萤火虫群优化算法和邻域粗糙集的属性约简方法.首先,运用反向学习协同初始化种群,并基于Sigmoid变化函数的映射进行二进制编码,引入Lévy飞行位置更新策略,提出改进二元萤火虫群优化算法.再以邻域粗糙集作为评价准则,以改进算法作为搜索策略,进行属性约简.最后,通过在标准UCI数据集上的实验验证属性约简方法的有效性,并验证文中算法具有较优的收敛速度和精度. |
| 关 键 词： | 属性约简 邻域粗糙集 二元萤火虫群优化算法 反向学习 Lévy飞行 |
| 收稿时间： | 2019-06-27 |
Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set |
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| PENG Peng,NI Zhiwei,ZHU Xuhui,XIA Pingfan. Attribute Reduction Method Based on Improved Binary Glowworm Swarm Optimization Algorithm and Neighborhood Rough Set[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(2): 95-105. DOI: 10.16451/j.cnki.issn1003-6059.202002001 | |
| Authors: | PENG Peng NI Zhiwei ZHU Xuhui XIA Pingfan |
| Affiliation: | 1. School of Management, Hefei University of Technology, Hefei 230009; 2. North Minzu University, Yinchuan 750021; 3. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei University of Technology, Hefei 230009 |
| Abstract: | Aiming at the problems of dimension reduction and redundancy removing,an attribute reduction method based on improved binary glowworm swarm optimization algorithm and neighborhood rough set is proposed.Firstly,the population is collaborative initialization using reverse learning,and the mapping of the change function based on Sigmoid is employed for binary coding,and an improved binary glowworm opti-mization algorithm is proposed with Levy flight position update strategy.Secondly,neighborhood rough set is employed as an evaluation criterion,and the proposed algorithm is utilized as an search strategy for attribute reduction.Finally,experiments on the standard UCI datasets demonstrate the effectiveness of the attribute reduction method,and the better convergence speed and accuracy of the proposed algorithm is verified. |
| Keywords: | Attribute Reduction Neighborhood Rough Set Binary Glowworm Swarm Optimization Algorithm Reverse Learning Levy Flight |
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