FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

@article{Xie2021FIVESFI,
  title={FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data},
  author={Yuexiang Xie and Zhen Wang and Yaliang Li and Bolin Ding and Nezihe Merve Gurel and Ce Zhang and Minlie Huang and Wei Lin and Jingren Zhou},
  journal={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
  year={2021}
}
  • Yuexiang Xie, Zhen Wang, +6 authors Jingren Zhou
  • Published 2021
  • Computer Science, Mathematics
  • Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate the generation of interactive features, existing works either explicitly traverse the feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and search efficiency… Expand

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