[1904.12857] AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applicationsopen searchopen navigation menucontact arXivarXiv Twitter

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of Au

1 mentions: @upura0
Date: 2020/05/21 11:21

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@upura0 なんかタイトルに既視感あると思ったら、これか AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications arXiv: t.co/bStwd65uoH YouTube: t.co/GLxC0NVQpB

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