Authors:
Yuki Mori
1
;
Seiji Yamada
2
and
Takashi Onoda
1
Affiliations:
1
Aoyama Gakuin University School of Science and Engineering, 5-10-1 Huchinobe, Chuo, Sagamihara, Kanagawa, Japan
;
2
National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda, Tokyo, Japan
Keyword(s):
Group LASSO Regression, Machine Learning, LASSO Regression, Group Variable Selection, Estimate Group Importance, Linear Regression Problem, Penalized Least Squares Problem.
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.