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Feature engineering, a crucial step of machine learning, aims to extractuseful features from raw data to improve data quality. In recent years, greatefforts have been devoted to Automated Feature Engineering (AutoFE) to replaceexpensive human labor. However, existing methods are computationally demandingdue to treating AutoFE as a coarse-grained black-box optimization problem overa discrete space. In this work, we propose an efficient gradient-based methodcalled DIFER to perform differentiable automated feature engineering in acontinuous vector space. DIFER selects potential features based on evolutionaryalgorithm and leverages an encoder-predictor-decoder controller to optimizeexisting features. We map features into the continuous vector space via theencoder, optimize the embedding along the gradient direction induced by thepredicted score, and recover better features from the optimized embedding bythe decoder. Extensive experiments on classification and regression datasetsdemonstrate that DIFER can significantly improve the performance of variousmachine learning algorithms and outperform current state-of-the-art AutoFEmethods in terms of both efficiency and performance.

DIFER: Differentiable Automated Feature Engineering