Self-Organizing Transformations for Automatic Feature Engineering

Silva Rodrigues E, Martins DML, Lima Neto FB


Abstract

Feature Engineering (FE) consists of generating new, better features to improve Machine Learning models. Very often, FE is performed in a series of trial-and-error steps conducted manually by data scientists. Moreover, FE requires data-specific and domain knowledge, both rarely easy to acquire. To alleviate these problems, we propose the Self-Organizing Automatic Feature Engineering (SOAFE), a novel approach for Automatic Feature Engineering (AFE). Different from the majority of the existing AFEs, SOAFE employs an unsupervised technique (Self-Organizing Maps) to identify patterns in the data, and apply a form of cooperative training, inspired by Generative Adversarial Networks, to improve the feature construction. Our results on several datasets show that SOAFE can improve classification models when compared with existing AFE approaches.

Keywords
feature engineering; automatic feature engineering; self-organizing maps; machine learning



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2021

Conference
IEEE Symposium Series on Computational Intelligence

Venue
Orlando

Book title
2021 IEEE Symposium Series on Computational Intelligence (SSCI)

Editor
unknown, unknown;

Start page
1

End page
7

Publisher
IEEE

Place
Orlando

Language
English

ISBN
978-1-7281-9048-8

DOI

Full text