Quality inspection is essential in preventing defective products from entering the market. Due to the typically low percentage of defective products, it is generally challenging to detect them using algorithms that aim for the overall classification accuracy. To help solve this problem, we propose an ensemble learning classification model, where we employ adaptive boosting (AdaBoost) to cascade multiple backpropagation (BP) neural networks. Furthermore, cost-sensitive (CS) learning is introduced to adjust the loss function of the basic classifier of the BP neural network. For clarity, this model is called a CS-AdaBoost-BP model. To empirically verify its effectiveness, we use data from home appliance production lines from Bosch. We carry out tenfold cross-validation to evaluate and compare the performance between the CS-AdaBoost-BP model and three existing models: BP neural network, BP neural network based on sampling, and AdaBoost-BP. The results show that our proposed model not only performs better than the other models but also significantly improves the ability to identify defective products. Furthermore, based on the mean value of the Youden index, our proposed model has the highest stability.

中文翻译：

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一种用于产品质量检测的 CS-AdaBoost-BP 模型

质量检验对于防止有缺陷的产品进入市场至关重要。由于缺陷产品的百分比通常很低，因此使用旨在提高整体分类准确性的算法来检测它们通常具有挑战性。为了帮助解决这个问题，我们提出了一个集成学习分类模型，我们采用自适应提升 (AdaBoost) 来级联多个反向传播 (BP) 神经网络。此外，引入成本敏感（CS）学习来调整BP神经网络基本分类器的损失函数。为清楚起见，此模型称为 CS-AdaBoost-BP 模型。为了实证验证其有效性，我们使用了博世家电生产线的数据。我们进行了十倍交叉验证来评估和比较 CS-AdaBoost-BP 模型与三个现有模型之间的性能：BP 神经网络、基于采样的 BP 神经网络和 AdaBoost-BP。结果表明，我们提出的模型不仅比其他模型表现更好，而且显着提高了识别缺陷产品的能力。此外，基于约登指数的平均值，我们提出的模型具有最高的稳定性。