MACFE: A Meta-learning and Causality Based Feature Engineering Framework
This work addresses the need for efficient automation in feature engineering for machine learning practitioners, offering a novel approach that is incremental but provides measurable performance gains.
The paper tackles the problem of automating feature engineering, which is time-consuming and requires domain knowledge, by proposing MACFE, a framework that uses meta-learning and causality-based feature selection. The result is an average improvement of at least 6.54% over state-of-the-art methods and a 2.71% gain over previous best works across eight classifiers on popular datasets.
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.