Greedy feature selection: Classifier-dependent feature selection via greedy methods
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913257999040512 |
|---|---|
| author | Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Piana, Michele Perracchione, Emma |
| author_facet | Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Piana, Michele Perracchione, Emma |
| contents | The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greedy feature selection identifies the most important feature at each step and according to the selected classifier. In the paper, the benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested numerically by considering its application to the problem of predicting geo-effective manifestations of the active Sun. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Greedy feature selection: Classifier-dependent feature selection via greedy methods Camattari, Fabiana Guastavino, Sabrina Marchetti, Francesco Piana, Michele Perracchione, Emma Machine Learning Numerical Analysis 68Q32, 68T07, 65D12 The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feature selection is usually realized by means of methods that are independent of the classifier applied to perform the prediction using that reduced number of features. Instead, greedy feature selection identifies the most important feature at each step and according to the selected classifier. In the paper, the benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment, and tested numerically by considering its application to the problem of predicting geo-effective manifestations of the active Sun. |
| title | Greedy feature selection: Classifier-dependent feature selection via greedy methods |
| topic | Machine Learning Numerical Analysis 68Q32, 68T07, 65D12 |
| url | https://arxiv.org/abs/2403.05138 |