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

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。

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.