MACFE: A Meta-learning and Causality Based Feature Engineering Framework

Feature engineering has become one of the most important steps to improving model prediction performance, and producing quality datasets. However, this process requires non-trivial domain knowledge which involves a time-consuming task. Thereby, automating such processes has become an active area of research and 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 on average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

MACFE: A Meta-learning and Causality Based Feature Engineering Framework Chapter in Scopus