一种深度学习的信息文本分类算法

AN Information Text Classification Algorithm Based on DBN

针对传统文本分类算法准确率低和正确率分布不均匀的问题,提出了基于深度学习的文本分类算法。深度信念网络具有强大的学习能力,可以从高维的原始特征中提取高度可区分的低维特征,不仅能够更全面的考虑到文本信息量,而且能够进行快速分类。采用TF-IDF方法计算文本特征值,利用深度信念网络构造分类器进行精准分类。实验结果表明,与支持向量机、神经网络和极端学习机等常用分类算法相比,该算法有更高的准确率和实用性,为文本的分类研究开拓了新思路。

Aiming at the problem of low categorization accuracy and uneven distribution of the traditional text classification algorithms, a text classification algorithm based on deep learning has been put forward. Deep belief networks have very strong feature learning ability, which can be extracted from the high dimension of the original feature, so that the text classification can not only be considered, but also can be used to train classification model. The formula of TF-IDF is used to compute text eigenvalues, and the deep belief networks are used to construct the classifier. The experimental results show that compared with the commonly used classification algorithms such as support vector machine, neural network and extreme learning machine, the algorithm has higher accuracy and practicability, and it has opened up new ideas for the research of text classification.