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Po Guan

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Proceedings Article | 21 December 2023 Paper

Research on infant behavior feature classification based on GWO-ELM algorithm

Mingshen Xu, Xinyu Shi, Po Guan, Zhixue Zhu

Proceedings Volume 12970, 129703Z (2023) https://doi.org/10.1117/12.3012230

KEYWORDS: Mathematical optimization, Data modeling, Random forests, Machine learning, Education and training, Extreme learning machines, Neural networks, Decision trees, Evolutionary algorithms, Statistical modeling

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Regarding the association between maternal mental and physical health and infant behavior, this paper proposes a method that integrates Grey Wolf Optimization (GWO) with Extreme Learning Machine (ELM) for the correlation and classification of infant behavioral features and maternal health indicators. Firstly, a dataset of maternal mental and physical health and infant behavioral features is established. Using the Random Forest method, physical and psychological indicators of the mother and their eight primary influencing factors are extracted from the dataset. Subsequently, the classification accuracy of infant behavior is used as the fitness function for the Grey Wolf algorithm. The GWO algorithm optimizes the kernel and regularization parameters of the ELM, which are then fed into the ELM to train the four key feature parameters of infant behavior. Finally, it is applied to the classification task of infant behavior. The results indicate that the classification method combining GWO and ELM has an accuracy of 86% and a precision of 91%. This outperforms traditional classification models such as XGBoost and GBDT in detection and classification. It also surpasses the performance of ELM optimized by traditional optimization algorithms like SSA and PSO, validating the feasibility and effectiveness of the proposed method.

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