Boosting gets full Attention for Relational Learning

• URL: http://arxiv.org/abs/2402.14926v1
• Date: Thu, 22 Feb 2024 19:16:01 GMT
• Title: Boosting gets full Attention for Relational Learning
• Authors: Mathieu Guillame-Bert and Richard Nock
• Abstract summary: We introduce an attention mechanism for structured data that blends well with tree-based models in the training context of (gradient) boosting. 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.
• Score: 27.82663283409287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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