SS-RNN: A Strengthened Skip Algorithm for Data Classification Based on Recurrent Neural Networks

Frontiers in Genetics

Ya-Zhou Shi¹  Bengong Zhang¹  Huahai Qiu¹  Wenjie Cao² 

[1] Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, China;School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China;

关键词: RNN;    LSTM;    SS-RNN;    data classification;    deep learning;   

DOI  :  10.3389/fgene.2021.746181

来源: DOAJ

【 摘 要 】

Recurrent neural networks are widely used in time series prediction and classification. However, they have problems such as insufficient memory ability and difficulty in gradient back propagation. To solve these problems, this paper proposes a new algorithm called SS-RNN, which directly uses multiple historical information to predict the current time information. It can enhance the long-term memory ability. At the same time, for the time direction, it can improve the correlation of states at different moments. To include the historical information, we design two different processing methods for the SS-RNN in continuous and discontinuous ways, respectively. For each method, there are two ways for historical information addition: 1) direct addition and 2) adding weight weighting and function mapping to activation function. It provides six pathways so as to fully and deeply explore the effect and influence of historical information on the RNNs. By comparing the average accuracy of real datasets with long short-term memory, Bi-LSTM, gated recurrent units, and MCNN and calculating the main indexes (Accuracy, Precision, Recall, and F1-score), it can be observed that our method can improve the average accuracy and optimize the structure of the recurrent neural network and effectively solve the problems of exploding and vanishing gradients.

【 授权许可】

Unknown