| 基于多目标蜂群算法的数据分类方法 |
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| 引用本文： | 王海泉,侯宇亮,魏建华,徐晓滨,苏孟豪,张姗姗.基于多目标蜂群算法的数据分类方法[J].重庆大学学报(自然科学版),2020,43(1):74-81. |
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| 作者姓名： | 王海泉 侯宇亮 魏建华 徐晓滨 苏孟豪 张姗姗 |
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| 作者单位： | 中原工学院 中原彼得堡航空学院, 郑州 450007,中原工学院 电子信息学院, 郑州 450007,中原工学院 电子信息学院, 郑州 450007,杭州电子 科技大学 自动化学院, 杭州 310018,中原工学院 电子信息学院, 郑州 450007,中原工学院 电子信息学院, 郑州 450007 |
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| 基金项目： | 河南省高等学校重点科研项目（18A120005）；浙江省重点研发项目（2019C03104）。 |
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| 摘 要： | 为了保证运算时效的同时,提高复杂数据的分类精度,提出了基于多目标蜂群算法和极限学习机的数据分类算法。该方法以最小的特征个数和最高的分类精度为优化目标,利用改进的多目标蜂群算法对数据的特征个数和分类器参数进行寻优,针对多个有代表性的数据集进行仿真,结果表明所提出方法的有效性。
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| 关 键 词： | 蜂群算法 多目标优化 特征选择 极限学习机 数据分类 |
| 收稿时间： | 2019/5/13 0:00:00 |
Research of data classification method based on multi-objective artificial bee colony algorithm |
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| WANG Haiquan,HOU Yuliang,WEI Jianhu,XU Xiaobin,SU Menghao and ZHANG Shanshan.Research of data classification method based on multi-objective artificial bee colony algorithm[J].Journal of Chongqing University(Natural Science Edition),2020,43(1):74-81. |
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| Authors: | WANG Haiquan HOU Yuliang WEI Jianhu XU Xiaobin SU Menghao and ZHANG Shanshan |
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| Institution: | Zhongyuan Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou 450007, P. R. China,School of Electronic Information, Zhongyuan University of Technology, Zhengzhou 450007, P. R. China,School of Electronic Information, Zhongyuan University of Technology, Zhengzhou 450007, P. R. China,School of Automation, Hangzhou Dianzi University, Hangzhou 310018, P. R. China,School of Electronic Information, Zhongyuan University of Technology, Zhengzhou 450007, P. R. China and School of Electronic Information, Zhongyuan University of Technology, Zhengzhou 450007, P. R. China |
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| Abstract: | In order to improve the classification accuracy of complex data on the premise of ensuring operation efficiency, a data classification algorithm based on multi-objective artificial bee colony algorithm and extreme learning machine is proposed, it takes the number of features and the classification accuracy as the optimization objectives, and improved artificial bee colony algorithm is introduced to optimize the parameters of the classifier and the selection of features of data. The simulation results based on six data sets verify the effectiveness of the proposed method. |
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| Keywords: | artificial bee colony algorithm multi-objective optimization feature selection extreme learning machine data classification |
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