Hypernetworks for Dynamic Feature Selection
Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constraints. The exponential growth in the number of possible feature acquisition paths forces a DFS model to balance fitting specific scenarios against maintaining general performance, even when the feature space is moderate in size. In this paper, we study the structural limitations of existing DFS approaches to achieve an optimal solution. Then, we propose \textsc{Hyper-DFS}, a hypernetwork-based DFS approach that generates feature subset-specific classifier parameters on demand. We show that the use of hypernetworks compared to mask-embedding ...
| Authors | Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez |
|---|---|
| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 28 September 2026 |
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