宋丹, 陆奎, 戴旭凡.基于改进的卷积神经网络邮件分类算法研究[J].重庆工商大学学报(自然科学版),2022,39(3):20-25
SONG Dan, LU Kui , DAI Xu-fan.Research on Mail Classification Algorithm Based on Improved Convolutional Neural Network[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2022,39(3):20-25
基于改进的卷积神经网络邮件分类算法研究
Research on Mail Classification Algorithm Based on Improved Convolutional Neural Network
  
DOI:
中文关键词:  文本分类  卷积神经网络  inception V1  word2vec
英文关键词:text classification  convolution neural network  inception V1  word2vec
基金项目:
作者单位
宋丹, 陆奎, 戴旭凡 1. 安徽理工大学计算机科学与工程学院, 安徽 淮南 232001
2. 安徽理工大学电气与信息工程学院, 安徽 淮南 232001 
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中文摘要:
      针对传统文本分类方法中出现的维度过高和数据稀疏问题,通过对卷积神经网络(Convolutional Neural Network,CNN)和 inception V1 模型的深入研究,将两个模型融合起来,提出了一种基于 i-CNN 模型的 邮件分类方法;在卷积、池化操作中加入了 1×1 卷积核降低特征向量的厚度,减少了参数,提高了计算性能; 通过数据验证,i-CNN 模型对邮件的分类结果高达 92. 18%,在对比实验中,i-CNN 模型相对于几种机器学 习分类模型,取得了最高的分类精准率,在有无 inception 结构模型对比中,i-CNN 模型精准率高于 CNN 模 型;说明该模型具有较好的分类效果,且 inception V1 模型的融入能提高文本分类的准确率。
英文摘要:
      Aiming at the problems of high dimension and sparse data in traditional text classification methods, this paper proposes an e-mail classification method based on i-CNN model by combining convolutional neural network (CNN) and inception V1 model. In the convolution and pooling operation, 1 × 1 convolution kernel is added to reduce the thickness of eigenvectors, reduce the parameters and improve the computational performance. Through data validation, the result of i-CNN model for e-mail classification reaches as high as 92. 18%. In the comparative experiment, compared with several machine learning classification models, i-CNN model achieved the highest classification accuracy. In the comparison with or without the inception structure model, i-CNN model accuracy is higher than CNN model. It shows that the model has a good classification effect, and the integration of inception V1 model can improve the accuracy of text classification.
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