Greedy feature selection: Classifier-dependent feature selection via greedy methods
<|reference_start|>Greedy feature selection: Classifier-dependent feature selection via greedy methods: 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.<|reference_end|>
@article{camattari2024greedy,
    title={Greedy feature selection: Classifier-dependent feature selection via
  greedy methods},
    author={Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Michele
  Piana, Emma Perracchione},
    journal={arXiv preprint arXiv:2403.05138},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05138},
    primaryClass={stat.ML cs.LG cs.NA math.NA}
}