Variable screening with binary quantum behavior particle swarm optimization

Xiaohong Peng; Rui Zheng; Jiufu Liu

doi:10.1117/12.2673581

22 May 2023 Variable screening with binary quantum behavior particle swarm optimization

Xiaohong Peng, Rui Zheng, Jiufu Liu

Author Affiliations +

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400B (2023) https://doi.org/10.1117/12.2673581
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

This paper proposes a Mahalanobis-Taguchi system variable optimization method based on binary quantum behavior particle swarm. Firstly, the Gram-Schmidt orthogonalization method is used to calculate the Mahalanobis distance value. Through the ROC curve, the optimal threshold point of the system classification is determined. The misclassification rate and the selected variables rate are defined, the multi-objective mixed 0-1 planning model is built.The improved quantum behavior particle swarm optimization algorithm is proposed to solve the optimization combination. To adapt to the binarization variable optimization problem, the algorithm performs binary coding on the particle based on probability. Using the optimized combination of variables, a new Mahalanobis-Taguchi metric basedprediction system is established to complete the task of precise discrimination.

Citation Download Citation

Xiaohong Peng, Rui Zheng, and Jiufu Liu "Variable screening with binary quantum behavior particle swarm optimization", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400B (22 May 2023); https://doi.org/10.1117/12.2673581

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