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Machine learning techniques have been widely applied in Internet companiesfor various tasks, acting as an essential driving force, and featureengineering has been generally recognized as a crucial tache when constructingmachine learning systems. Recently, a growing effort has been made to thedevelopment of automatic feature engineering methods, so that the substantialand tedious manual effort can be liberated. However, for industrial tasks, theefficiency and scalability of these methods are still far from satisfactory. Inthis paper, we proposed a staged method named SAFE (Scalable Automatic FeatureEngineering), which can provide excellent efficiency and scalability, alongwith requisite interpretability and promising performance. Extensiveexperiments are conducted and the results show that the proposed method canprovide prominent efficiency and competitive effectiveness when comparing withother methods. What's more, the adequate scalability of the proposed methodensures it to be deployed in large scale industrial tasks.

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks