Boosting gets full Attention for Relational Learning
<|reference_start|>Boosting gets full Attention for Relational Learning: More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single $m \times d$ (rows, columns) file, but cases abound in the real world where observations are described by a set of tables with structural relationships. Neural nets-based deep models are a classical fit to incorporate general topological dependence among description features (pixels, words, etc.), but their suboptimality to tree-based models on tabular data is still well documented. In this paper, we introduce an attention mechanism for structured data that blends well with tree-based models in the training context of (gradient) boosting. Each aggregated model is a tree whose training involves two steps: first, simple tabular models are learned descending tables in a top-down fashion with boosting's class residuals on tables' features. Second, what has been learned progresses back bottom-up via attention and aggregation mechanisms, progressively crafting new features that complete at the end the set of observation features over which a single tree is learned, boosting's iteration clock is incremented and new class residuals are computed. Experiments on simulated and real-world domains display the competitiveness of our method against a state of the art containing both tree-based and neural nets-based models.<|reference_end|>
@article{guillame-bert2024boosting,
    title={Boosting gets full Attention for Relational Learning},
    author={Mathieu Guillame-Bert and Richard Nock},
    journal={arXiv preprint arXiv:2402.14926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14926},
    primaryClass={cs.LG}
}