[1]钟桂凤,庞雄文,孙道宗.基于差分进化的卷积神经网络的文本分类研究[J].南京师大学报(自然科学版),2022,45(01):136-141.[doi:10.3969/j.issn.1001-4616.2022.01.019]
　Zhong Guifeng,Pang Xiongwen,Sun Daozong.Research on Text Classification Based on Convolutional Neural Network of Differential Evolution[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(01):136-141.[doi:10.3969/j.issn.1001-4616.2022.01.019]
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基于差分进化的卷积神经网络的文本分类研究()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第45卷
期数:
2022年01期
页码:
136-141
栏目:
·计算机科学与技术·
出版日期:
2022-03-15

文章信息/Info

Title:
Research on Text Classification Based on Convolutional Neural Network of Differential Evolution
文章编号:
1001-4616(2022)01-0136-06
作者:
钟桂凤1; 庞雄文2; 孙道宗3
(1.广州理工学院计算机科学与工程学院,广东 广州 510540)(2.华南师范大学计算机学院,广东 广州 530631)(3.华南农业大学 电子工程学院,广东 广州 510642)
Author(s):
Zhong Guifeng1; Pang Xiongwen2; Sun Daozong3
(1.College of Computer Science & Engineering,Guangzhou Institute of Science and Technology,Guangzhou 530631,China)(2.School of Computer Science,South China Normal University,Guangzhou 530631,China)(3.School of Electronic Engineering,South China Agricultural University,Guangzhou 530631,China)
关键词:
分类; 差分进化; 卷积神经网络; 缩放因子; 卷积核
Keywords:
classification; differential evolution; convolution neural network; scaling factor; convolution kernel
分类号:
TP391.1
DOI:
10.3969/j.issn.1001-4616.2022.01.019
文献标志码:
A
摘要:
为了提高文本分类的性能,采用差分进化的卷积神经网络(convolutional neural network,CNN)算法进行分类. 首先随机设置CNN结构参数,然后采用差分进化算法优化参数,通过交叉和选择等操作选择不断进化获得最优个体,为增强差分优化的适用性,将缩放因子变化与进化代数相关联,解决了因为缩放因子设置不合理而造成优化等级不高的问题. 卷积神经网络采用经过差分优化后的权重和阈值对文本进行分类训练,以获得稳定的文本分类结果. 实验证明,通过合理设置差分进化交叉速率和卷积神经网络的卷积核尺寸,能够获得较好的分类准确率性能,RMSE值更低,在文本分类中的适用度高.
Abstract:
In order to improve the performance of text classification,differential evolution convolutional neural network(CNN)algorithm was used for text classification. Firstly,the structural parameters of CNN were set randomly,then the parameters were optimized by differential evolution algorithm,and the optimal individuals were obtained by continuous evolution through crossover and selection. In order to enhance the applicability of differential optimization,the change of scaling factor was associated with evolutionary algebra,which solved the problem of low optimization level caused by unreasonable setting of scaling factor. Convolutional neural network used the weight and threshold after differential optimization to train text classification,so as to obtain stable text classification results. Experimental results showed that by setting the crossover rate of differential evolution and convolution kernel size of convolution neural network reasonably,better classification accuracy performance can be obtained,RMSE value was lower,and applicability in text classification was high.

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备注/Memo

备注/Memo:
收稿日期:2021-05-07.
基金项目:国家自然科学基金项目(31101077)、2020年度广东省高校科研项目(2020GXJK201).
通讯作者:钟桂凤,讲师,研究方向:数据分析与挖掘,人工智能,机器学习. E-mail:109488818@qq.com
更新日期/Last Update: 1900-01-01