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Boosting gets full Attention for Relational Learning
Feb. 26, 2024, 5:41 a.m. | Mathieu Guillame-Bert, Richard Nock
cs.LG updates on arXiv.org arxiv.org
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 …
abstract arxiv attention benchmark boosting cases cs.lg data file neural nets relational relationships set tables tabular tabular data type world
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