Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashing

• URL: http://arxiv.org/abs/2502.20293v1
• Date: Thu, 27 Feb 2025 17:17:53 GMT
• Title: Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashing
• Authors: Zahiriddin Rustamov, Ayham Zaitouny, Nazar Zaki,
• Abstract summary: Instance selection (IS) is important in machine learning for reducing dataset size while keeping key characteristics.<n>This paper introduces a graph attention-based instance selection (GAIS) method that uses attention mechanisms to identify informative instances.<n>We present two approaches for scalable graph construction: a distance-based mini-batch sampling technique that reduces through strategic batch processing, and a hierarchical hashing approach that allows for efficient similarity through random projections.
• Score: 0.24578723416255752
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Instance selection (IS) is important in machine learning for reducing dataset size while keeping key characteristics. Current IS methods often struggle with capturing complex relationships in high-dimensional spaces and scale with large datasets. This paper introduces a graph attention-based instance selection (GAIS) method that uses attention mechanisms to identify informative instances through their structural relationships in graph representations. We present two approaches for scalable graph construction: a distance-based mini-batch sampling technique that reduces computation through strategic batch processing, and a hierarchical hashing approach that allows for efficient similarity computation through random projections. The mini-batch approach keeps class distributions through stratified sampling, while the hierarchical hashing method captures relationships at multiple granularities through single-level, multi-level, and multi-view variants. Experiments across 39 datasets show that GAIS achieves reduction rates above 96\% while maintaining or improving model performance relative to state-of-the-art IS methods. The findings shows that the distance-based mini-batch approach offers an optimal balance of efficiency and effectiveness for large-scale datasets, while multi-view variants provide superior performance for complex, high-dimensional data, demonstrating that attention-based importance scoring can effectively identify instances crucial for maintaining decision boundaries without requiring exhaustive pairwise comparisons.

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