1 research outputs found
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks
Machine learning techniques have been widely applied in Internet companies
for various tasks, acting as an essential driving force, and feature
engineering has been generally recognized as a crucial tache when constructing
machine learning systems. Recently, a growing effort has been made to the
development of automatic feature engineering methods, so that the substantial
and tedious manual effort can be liberated. However, for industrial tasks, the
efficiency and scalability of these methods are still far from satisfactory. In
this paper, we proposed a staged method named SAFE (Scalable Automatic Feature
Engineering), which can provide excellent efficiency and scalability, along
with requisite interpretability and promising performance. Extensive
experiments are conducted and the results show that the proposed method can
provide prominent efficiency and competitive effectiveness when comparing with
other methods. What's more, the adequate scalability of the proposed method
ensures it to be deployed in large scale industrial tasks