ANALYSIS OF NOVEL FEATURE SELECTION CRITERION BASED ON INTERACTIONS OF HIGHER ORDER IN CASE OF PRODUCTION PLANT DATA

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Opublikowane:
Nov 15, 2019
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Tom 20 Nr 3 (2019)
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Mateusz Pawluk
Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland
https://orcid.org/0000-0003-4969-4237
Dariusz Wierzba
Faculty of Economic Sciences, University of Warsaw, Poland
https://orcid.org/0000-0001-5829-453X

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

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Pawluk, M., & Wierzba, D. (2019). ANALYSIS OF NOVEL FEATURE SELECTION CRITERION BASED ON INTERACTIONS OF HIGHER ORDER IN CASE OF PRODUCTION PLANT DATA. Metody Ilościowe W Badaniach Ekonomicznych, 20(3), 209–216. https://doi.org/10.22630/MIBE.2019.20.3.20
Bibliografia

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