2024/12/26 by Zahiriddin Rustamov, Rustamov, Zahiriddin, Ayham Zaitouny +5
Computer Science · #Artificial intelligence #Computer science #Graph #Graph Theory and Algorithms #Machine Learning and Data Classification #Selection (genetic algorithm) #Theoretical computer science #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2412.19201
openalex publication_date 2024/12/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Instance selection (IS) is a crucial technique in machine learning that aims to reduce dataset size while maintaining model performance. This paper introduces a novel method called Graph Attention-based Instance Selection (GAIS), which leverages Graph Attention Networks (GATs) to identify the most informative instances in a dataset. GAIS represents the data as a graph and uses GATs to learn node representations, enabling it to capture complex relationships between instances. The method processes data in chunks, applies random masking and similarity thresholding during graph construction, and selects instances based on confidence scores from the trained GAT model. Experiments on 13 diverse datasets demonstrate that GAIS consistently outperforms traditional IS methods in terms of effectiveness, achieving high reduction rates (average 96%) while maintaining or improving model performance. Although GAIS exhibits slightly higher computational costs, its superior performance in maintaining accuracy with significantly reduced training data makes it a promising approach for graph-based data selection.