CAMGnet: A self-learning classification model for univariate continuous time series signals via dynamic fusion of multi-dimensional cross-domain features
IF 6.5 2区 计算机科学Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0
Abstract
Improvements in classifying continuous, univariate time series signals with low value density have been hindered by insufficient feature detail, inadequate modeling of cross-domain dynamics, and inefficient parameter optimization. To address this, we propose a self-learning Cross-domain Adaptive Multi-dimensional Fusion Graph Neural Network (CAMGnet). We construct a Temporal Synergetic Pyramid (TSP) module to hierarchically extract time domain features from short-term to long-term trends. We develop an Entropy-adaptive Graph Construction (EAGC) mechanism to model cross-domain feature correlations. EAGC dynamically infers implicit feature-space/graph-topology relationships using self-adaptive adjacency matrices, minimizing reliance on prior knowledge and enabling autonomous, data-driven discovery of cross-domain interactions. Graph Convolutional Network–Graph Isomorphism Network(GCN-GIN) based hybrid encoding facilitates deep collaborative optimization between feature spaces and graph topologies. We also develop a Competitive Cross-attention (CCA) fusion mechanism to perform competitive multi-modal feature selection, enabling temporal/multi-domain graphs to capture cross-modal dependencies. Furthermore, we propose an Adaptive Perturbation Dynamic Escape Exploration–Exploitation Co-evolutionary Pool Strategy (AEP-IVYA) for improving the Ivy Optimization Algorithm.Adaptive perturbation balances exploration-exploitation by dynamically adjusting perturbation parameters. The dynamic escape strategy introduces a cross-dimensional transition mechanism to overcome local optima. The co-evolutionary pool uses a dual-path architecture to optimize global diversity and convergence. Evaluated on weld seam defect diagnosis and UCR datasets, AEP-IVYA improved hyperparameter configuration reliability. The self-optimized CAMGnet achieved 98.7 % accuracy in weld defect classification, surpassing traditional methods by 8.1 percentage points. On 25 UCR datasets, CAMGnet achieved 13 optimal and 6 suboptimal results, with a Wilcoxon-test average rank of 2.0, demonstrating significant generalization and domain applicability advantages over mainstream models.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.