  Feature engineering has become one of the most important steps to improve
model prediction performance, and to produce quality datasets. However, this
process requires non-trivial domain-knowledge which involves a time-consuming
process. Thereby, automating such process has become an active area of research
and of interest in industrial applications. In this paper, a novel method,
called Meta-learning and Causality Based Feature Engineering (MACFE), is
proposed; our method is based on the use of meta-learning, feature distribution
encoding, and causality feature selection. In MACFE, meta-learning is used to
find the best transformations, then the search is accelerated by pre-selecting
"original" features given their causal relevance. Experimental evaluations on
popular classification datasets show that MACFE can improve the prediction
performance across eight classifiers, outperforms the current state-of-the-art
methods in average by at least 6.54%, and obtains an improvement of 2.71% over
the best previous works.
