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Found 10 papers, 3 papers with code
Feature selection in linear SVMs via hard cardinality constraint: a scalable SDP decomposition approach
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 a fully explainable selection model.
The forgotten pillar of sustainability: development of the S-assessment tool to evaluate Organizational Social Sustainability
Pursuing sustainable development has become a global imperative, underscored adopting of the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDG).
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
We evaluated these models' robustness against Out-of-Distribution drugs in the test set, with a specific focus on the GNN's role in handling such scenarios.
Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1
Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis.
Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree
First, given a target tree-ensemble model, we develop a hierarchical visualization tool based on a heatmap representation of the forest's feature use, considering the frequency of a feature and the level at which it is selected as an indicator of importance.
Convergence under Lipschitz smoothness of ease-controlled Random Reshuffling gradient Algorithms
We aim to define ease-controlled modifications of the IG/RR schemes, which require a light additional computational effort and can be proved to converge under very weak and standard assumptions.
Margin Optimal Classification Trees
Thanks to their interpretability, decision trees have been intensively studied for classification tasks and, due to the remarkable advances in mixed integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model.
Solving the vehicle routing problem with deep reinforcement learning
Comparisons between the algorithm proposed and the state-of-the-art solver OR-TOOLS show that the latter still outperforms the Reinforcement learning algorithm.
AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database.
Block Layer Decomposition schemes for training Deep Neural Networks
Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus.
