Group Importance Estimation Method Based on Group LASSO Regression

Yuki Mori, Seiji Yamada, Takashi Onoda

2024

Abstract

There has been a rapidly growing interest in penalized least squares problems via l 1 regularization. The LASSO (Least Absolute Shrinkage and Selection Operator) regression, which utilizes l1 regularization, has gained popularity as a method for model selection and shrinkage estimation. An important extension of LASSO regression is Group LASSO regression, which generates sparse models at the group level. However, Group LASSO regression does not directly evaluate group importance. In this study, we propose a method to assess group importance based on Group LASSO regression. This method leverages regularization parameters to estimate the importance of each group. We applied this method to both synthetically generated data and real-world data, conducting experiments to evaluate its performance. As a result, the method accurately approximated the importance of groups, enhancing the interpretability of models at the group level.

Paper Citation

in Harvard Style

Mori Y., Yamada S. and Onoda T. (2024). Group Importance Estimation Method Based on Group LASSO Regression. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 197-204. DOI: 10.5220/0012304800003654

in Bibtex Style

@conference{icpram24,
author={Yuki Mori and Seiji Yamada and Takashi Onoda},
title={Group Importance Estimation Method Based on Group LASSO Regression},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={197-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012304800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Group Importance Estimation Method Based on Group LASSO Regression
SN - 978-989-758-684-2
AU - Mori Y.
AU - Yamada S.
AU - Onoda T.
PY - 2024
SP - 197
EP - 204
DO - 10.5220/0012304800003654
PB - SciTePress