LESO: Latent Entropy-guided Synthetic Oversampling
Abstract
Initial public release of LESO (Latent Entropy-guided Synthetic Oversampling). This release provides: A command-line tool for entropy-guided oversampling of binary imbalanced datasets CSV-based input/output for easy integration Reproducible, seed-controlled synthetic data generation This version corresponds to the experimental implementation used in the accompanying manuscript.
Fields of Study & Subjects
Imbalanced Data Classification Techniques
Machine Learning and Data Classification
Data Stream Mining Techniques
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 29 January 2026