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A CS-AdaBoost-BP model for product quality inspection

Author

Listed:
  • Zengyuan Wu

    (China Jiliang University)

  • Caihong Zhou

    (Chongqing University)

  • Fei Xu

    (Wilfrid Laurier University)

  • Wengao Lou

    (Shanghai Business School)

Abstract

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.

Suggested Citation

  • Zengyuan Wu & Caihong Zhou & Fei Xu & Wengao Lou, 2022. "A CS-AdaBoost-BP model for product quality inspection," Annals of Operations Research, Springer, vol. 308(1), pages 685-701, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03798-z
    DOI: 10.1007/s10479-020-03798-z
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    References listed on IDEAS

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    Cited by:

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