Modern synergetic neural network for imbalanced small data classification

• Published in: Scientific reports Vol. 13; no. 1; pp. 15669 - 10
• Main Authors: Wang, Zihao, Li, Haifeng, Ma, Lin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.09.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 639/705/117; Algorithms; Classification; Deep learning; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Neural networks; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-42689-8

Deep learning’s performance on the imbalanced small data is substantially degraded by overfitting. Recurrent neural networks retain better performance in such tasks by constructing dynamical systems for robustness. Synergetic neural network (SNN), a synergetic-based recurrent neural network, has superiorities in eliminating recall errors and pseudo memories, but is subject to frequent association errors. Since the cause remains unclear, most subsequent studies use genetic algorithms to adjust parameters for better accuracy, which occupies the parameter optimization space and hinders task-oriented tuning. To solve the problem and promote SNN’s application capability, we propose the modern synergetic neural network (MSNN) model. MSNN solves the association error by correcting the state initialization method in the working process, liberating the parameter optimization space. In addition, MSNN optimizes the attention parameter of the network with the error backpropagation algorithm and the gradient bypass technique to allow the network to be trained jointly with other network layers. The self-learning of the attention parameter empowers the adaptation to the imbalanced sample size, further improving the classification performance. In 75 classification tasks of small UC Irvine Machine Learning Datasets, the average rank of the MSNN achieves the best result compared to 187 neural and non-neural network machine learning methods.