Hi-LASSO
Hi-LASSO(High-Dimensional LASSO) can theoretically improves a LASSO model providing better performance of both prediction and feature selection on extremely high-dimensional data. Hi-LASSO alleviates bias introduced from bootstrapping, refines importance scores, improves the performance taking advantage of global oracle property, provides a statistical strategy to determine the number of bootstrapping, and allows tests of significance for feature selection with appropriate distribution. In Hi - LASSO will be applied to Use the pool of the python library to process parallel multiprocessing to reduce the time required for the model.
Citation
@article{article,
author = {Kim, Youngsoon and Hao, Jie and Mallavarapu, Tejaswini and Park, Joongyang and Kang, Mingon},
year = {2019},
month = {01},
pages = {44562-44573},
title = {Hi-LASSO: High-Dimensional LASSO},
volume = {7},
journal = {IEEE Access},
doi = {10.1109/ACCESS.2019.2909071}
}