If the dimension of feature space is very large, LASSO logistic regression may be challenged because too many variables may lead to overfitting and model instability. By contrast, reducing the dimension of feature space would be benefit for reduc...-Go服务器开发

Here are some references related to using LASSO logistic regression and feature selection for reducing model complexity:

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1-22.
Wang, S., Nan, B., Rosset, S., & Zhu, J. (2013). Random lasso. Annals of Applied Statistics, 7(1), 435-463.
Zhang, Y., Liu, L., Zhou, Y.-H., & Zhao, Q.-H. (2020). A novel hybrid approach integrating feature selection and machine learning algorithms for identifying potential biomarkers in gastric cancer prognosis prediction. Scientific Reports, 10(1), 1-13.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.
Huang C.L., Wang C.J.; A GA-based feature selection and parameters optimization for support vector machines; Expert Systems with Applications; Volume 31 Issue 2 Pages:231–240

I hope these references are helpful!