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GDP-CT: Grouped data pruning for camera trap

delete2025-11-02
delete0
PRE
AI
L
Lianshuai Sha
Y
Ye Tian
J
Junguo Zhang
DOI：10.1016/j.eswa.2025.130234delete
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Abstract

Abstract

En 中文
• General-purpose data pruning methods fail on camera trap datasets. • Imbalanced data and species-specific difficulties degrade pruning results. • Pruning by species and location groups boosts subset effectiveness. • Grouped data pruning offers model-agnostic stability and better efficiency. • Plug-and-play grouped pruning integrates easily with ecology human-AI workflows.

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
3.0W
Citations:
10.2W

Organization

B
beijing forestry university
Scholars:
1.9W
Papers: 1.1W
Citations: 3
Cited Papers

Cited Papers

No cited papers available