After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time‐consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel‐based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost‐sensitive deep learning model: cost‐sensitive siamese network (CSS‐Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost‐sensitive classification problem. We use optimization algorithms such as NSGA‐II to determine the optimal cost‐sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real‐production scenario. Furthermore, our model also outperforms its state‐of‐the‐art competitor models in other comprehensive cost‐sensitive metrics, with an average of 33.32% shorter training time.

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.