Analysis of Novel Feature Selection Criterion based on Interactions of Higher Order in Case of Production Plant Data

Czasopismo

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• Autorzy: Mateusz Pawluk , Dariusz Wierzba
• Języki publikacji: EN

Abstrakty

EN

Feature selection plays vital role in the processing pipeline of today's data science applications and is a crucial step of the overall modeling process. Due to multitude of possibilities for extracting large and highly structured data in various fields, this is a serious issue in the area of machine learning without any optimal solution proposed so far. In recent years, methods based on concepts derived from information theory attracted particular attention, introducing eventually general framework to follow. The criterion developed by author et al., namely IIFS (Interaction Information Feature Selection), extended state-of-the-art methods by adopting interactions of higher order, both 3-way and 4-way. In this article, careful selection of data from industrial site was made in order to benchmark such approach with others. Results clearly show that including side effects in IIFS can reorder output set of features significantly and improve overall estimate of error for the selected classifier. (original abstract)

Słowa kluczowe

EN

Machine learning; Variables selection

PL

Uczenie maszynowe; Dobór zmiennych

• Czasopismo: ---
• Strony: 209--216

Twórcy

autor

Mateusz Pawluk

• Warsaw University of Technology, Poland

autor

Dariusz Wierzba

• University of Warsaw, Poland

Bibliografia

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Identyfikatory

DOI

https://doi.org/10.22630/MIBE.2019.20.3.20