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

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.

GAIS: A Novel Approach to Instance Selection with Graph Attention Networks

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.

GAT-RWOS: Graph Attention-Guided Random Walk Oversampling for Imbalanced Data Classification

Class imbalance poses a significant challenge in machine learning (ML), often leading to biased models favouring the majority class. In this paper, we propose GAT-RWOS, a novel graph-based oversampling method that combines the strengths of Graph Attention Networks (GATs) and random walk-based oversampling. GAT-RWOS leverages the attention mechanism of GATs to guide the random walk process, focusing on the most informative neighbourhoods for each minority node. By performing attention-guided random walks and interpolating features along the traversed paths, GAT-RWOS generates synthetic minority samples that expand class boundaries while preserving the original data distribution. Extensive experiments on a diverse set of imbalanced datasets demonstrate the effectiveness of GAT-RWOS in improving classification performance, outperforming state-of-the-art oversampling techniques. The proposed method has the potential to significantly improve the performance of ML models on imbalanced datasets and contribute to the development of more reliable classification systems.