Automatic Feature Engineering Using Self-Organizing Maps

Silva Rodrigues E, Martins DML, Lima Neto FB


Zusammenfassung

Feature Engineering (FE) consists of generating new, better features to improve the results obtained by 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 an automatic FE approach based on Self-Organizing Maps (SOM) in which new features are generated via pattern recognition. The use of the SOM algorithm in variable generation tasks can identify data elements that help Machine Learning models to obtain better results and points out to a broad direction for future researches.

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



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2021

Konferenz
2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

Konferenzort
Temuco

Buchtitel
{IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021

Herausgeber
Unknown, Unknown;

Erste Seite
1

Letzte Seite
6

Verlag
IEEE

Ort
Temuco, Chile

ISBN
978-1-7281-8864-5

DOI

Gesamter Text