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

TL;DR

MACFE is a novel framework that automates feature engineering by combining meta-learning, causality, and feature distribution encoding, significantly improving classification performance and outperforming existing methods.

Contribution

The paper introduces MACFE, a new automated feature engineering approach that integrates meta-learning and causality to select optimal transformations and features.

Findings

MACFE improves prediction accuracy across eight classifiers.

It outperforms state-of-the-art methods by at least 6.54%.

Achieves a 2.71% improvement over previous best methods.

Abstract

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.