Text Classification Based on Improved Information Gain Algorithm and Convolutional Neural Network

Feature selection is an important step. It aims to filter some irrelevant features, improve the classifier speed and also reduce the interference during text classification process. Information gain (IG) feature selection algorithm is one of the most effective feature selection algorithms. But it is easy to filter out the characteristic words which have a low IG score but have a strong ability of text type identification. Because IG algorithm only considers the number of documents of feature items in each category. Aiming at this defect, we propose an improved information gain algorithm by introducing three parameters: intra-class word frequency, inter-class separation degree and intra-class dispersion degree. Then, the improved IG algorithm is used for feature selection, and important feature words with high IG value are selected according to the threshold value. Final, the important feature words in the text are expressed as two-dimensional word vectors and input into Convolutional Neural Network (CNN) to train and classify them. Therefore, a text classification model based on improved information gain and convolutional neural network is proposed and abbreviated as “I-CNN”. Through experiments, we achieve good experimental results in THUCNews Chinese text classification corpus. Experimental results prove that the improved IG algorithm is better than the traditional feature selection algorithm.