1. School of Computer Science and Engineering, Anhui University of Science & Technology, Anhui Huainan 232001, China;2. School of Electrical and Information Engineering, Anhui University of Science & Technology, Anhui Huainan 232001, China
Abstract:
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.
