12 citations · 12 across the 4 of their papers we have counts for
4 papers
Feature Selection in Nonlinear SVMs via Local Search and Submodular Optimization
Federico D'Onofrio, Yuri Faenza, Laura Palagi
Embedded feature selection is a classical approach to interpretable machine learning, aiming to iden- tify the most informative variables while simultaneously training the predicti…
Feature selection in linear SVMs via a hard cardinality constraint: a scalable SDP decomposition approach
Immanuel Bomze, Federico D'Onofrio, Laura Palagi +1
In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable…
The Incremental Knapsack Problem with Monotone Submodular All-or-Nothing Profits
Federico D'Onofrio, Yuri Faenza, Lingyi Zhang
We study incremental knapsack problems with profits given by a special class of monotone submodular functions, that we dub all-or-nothing. We show that these problems are not harde…
Margin Optimal Classification Trees
Federico D'Onofrio, Giorgio Grani, Marta Monaci +1
In recent years, there has been growing attention to interpretable machine learning models which can give explanatory insights on their behaviour. Thanks to their interpretability,…