Select High-Level Features: Efficient Experts from a Hierarchical Classification Network

arXiv:2403.05601v1 Announce Type: new
Abstract: This study introduces a novel expert generation method that dynamically reduces task and computational complexity without compromising predictive performance. It is based on a new hierarchical classification network topology that combines sequential processing of generic low-level features with parallelism and nesting of high-level features. This structure allows for the innovative extraction technique: the ability to select only high-level features of task-relevant categories. In certain cases, it is possible to skip almost all unneeded high-level features, …

abstract arxiv classification complexity computational cs.lg expert experts features hierarchical low network novel performance predictive processing study topology type