Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications
The exponential growth of big data has intensified the need for efficient and interpretable machine learning models that can handle diverse data characteristics while maintaining computational efficiency. Knowledge distillation has primarily focused on neural network-to-neural network transfer, leaving cross-paradigm knowledge transfer largely unexplored. This paper presents the first comprehensive study of bidirectional knowledge distillation between Random Forests (RF) and Deep Neural Networks (DNN), addressing critical gaps in ensemble learning and model compression for big data applications. We propose novel methodologies including progressive multi-stage distillation, multi-teacher e...
| Authors | Mahdi Naser Moghadasi |
|---|---|
| Primary category | cs.LG |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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