Scholarlink

<p>Graph neural networks are becoming increasingly popular in deep learning due to their ability to process data in irregular structures and graphs thus preserving additional spatial dependencies due to the arrangement of nodes. This network allows for better accuracy results in classification problems, where information from neighbors is also crucial. However, training such a model is complex and time-consuming and challenging. To date, some metaheuristic algorithms have been used primarily to optimize convolutional networks and find suitable hyperparameters, these are: genetic algorithm, particle swarm optimization, differential evolution or covariance matrix adaptation evolution strategy. In this paper, we propose a metaheuristic algorithm of Simulated Annealing with Uniform distribution for optimization of weights in GCNs, as a hybrid in combination with gradient optimizers. The performance of our technique was tested on the QM7 Dataset, where it was split into two datasets: imbalanced and balanced. Experimental results confirm that our proposed optimization method outperformed other standalone SOTA optimization models, including gradient and heuristics methods, demonstrating in each case to lower loss function values, higher accuracy values for balanced dataset and higher AUC (macro) values for imbalanced dataset.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月20日

查看全文
  • Agnieszka Polowczyk,
  • Alicja Polowczyk,
  • Marcin Woźniak

摘要

Graph neural networks are becoming increasingly popular in deep learning due to their ability to process data in irregular structures and graphs thus preserving additional spatial dependencies due to the arrangement of nodes. This network allows for better accuracy results in classification problems, where information from neighbors is also crucial. However, training such a model is complex and time-consuming and challenging. To date, some metaheuristic algorithms have been used primarily to optimize convolutional networks and find suitable hyperparameters, these are: genetic algorithm, particle swarm optimization, differential evolution or covariance matrix adaptation evolution strategy. In this paper, we propose a metaheuristic algorithm of Simulated Annealing with Uniform distribution for optimization of weights in GCNs, as a hybrid in combination with gradient optimizers. The performance of our technique was tested on the QM7 Dataset, where it was split into two datasets: imbalanced and balanced. Experimental results confirm that our proposed optimization method outperformed other standalone SOTA optimization models, including gradient and heuristics methods, demonstrating in each case to lower loss function values, higher accuracy values for balanced dataset and higher AUC (macro) values for imbalanced dataset.