no code implementations • 4 Aug 2023 • Alberto Archetti, Francesca Ieva, Matteo Matteucci
Our results underscore the potential of FedSurF++ to improve the scalability and effectiveness of survival analysis in distributed settings while preserving user privacy.
no code implementations • 20 Jun 2021 • Michela C. Massi, Francesca Ieva
Considering multi-channel trial recordings as statistical units and the EEG decoding task as the class of reference, the algorithm (i) exploits channel-specific 1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a supervised fashion to maximize class separability; (ii) it reduces a high dimensional multi-channel trial representation into a unique trial vector by concatenating the channels' embeddings and (iii) recovers the complex inter-channel relationships during channel selection, by exploiting an ensemble of AutoEncoders (AE) to identify from these vectors the most relevant channels to perform classification.
no code implementations • 22 Mar 2021 • Michela C. Massi, Francesca Ieva, Francesca Gasperoni, Anna Maria Paganoni
To achieve FS advantages in this setting, we propose a filtering FS algorithm ranking feature importance on the basis of the Reconstruction Error of a Deep Sparse AutoEncoders Ensemble (DSAEE).
no code implementations • 23 Feb 2021 • Michela C. Massi, Nicola R. Franco, Francesca Ieva, Andrea Manzoni, Anna Maria Paganoni, Paolo Zunino
The algorithm relies on an interaction learning step based on a well-known frequent item set mining algorithm, and a novel dissimilarity-based interaction selection step that allows the user to specify the number of interactions to be included in the LR model.