A Feature Selection Method Based on a Convolutional Neural Network for Text Classification

Feature selection, the technique to distinguish discriminative terms among large quantities of irrelevant and noisy terms in high-dimensional text data, is the effective solution for reducing computational cost and improving the performance of text classification. To address the issues of effective feature selection in text classification, a new ranking feature selection method is developed by utilizing the deep learning model convolutional neural network (CNN). Our method uses a pre-trained three-layer CNN to capture deep features of terms and selects discriminative terms according to the combination of deep features and term frequencies, aiming to improve performance of feature selection for text classification. Moreover, the CNN training in our method is relatively fast because the loss function of the CNN does not need to reach convergence. Nine benchmark datasets and several comparative methods are introduced to validate the effectiveness of our method. Experimental results demonstrate the following: (1) Our method achieves better performance than other comparative methods in improving text classification accuracy, with significance level of 0.05 in most cases. (2) The top 20 terms selected by our method are more semantically related to the topics of datasets than comparative methods. (3) Our method outperforms comparative methods in reducing the sparsity of text. The sparsity reduction effect generally falls in [2%, 8%].