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Detail publikace
ŠTOHL, R. STIBOR, K.
Originální název
Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Considering the extensive data sets and statistical techniques, a digital factory (plant) embodies a branch of machine learning that has an impact on machine safety. We propose a study based on an application of decision trees and rules algorithms (JRIP, J48, Random Forest, Random Tree, and PART). Our experimental data were collected from various industrial machine safety solutions. Diverse validation techniques were employed to derive the classification performance of each method; the approach is expected to simplify the user choice of a suitable safety logic device type. In this study, the overall classification methods proportion of individual safety logic device solutions were correctly assigned by using the training-evaluated test mode, and the prediction accuracy reached 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 82% (JRIP and PART). PART as the best method was correctly assigned for the 10-fold cross-validation test mode (85%). New developments within the broad province of machine learning, including the concepts characterized in our study, may facilitate effective assessment of machine safety systems.
Klíčová slova
assignment success; safety; risk assessment; decision tree; rules algorithm; WEKA; JRip; J48; Random Forest; Random Tree; PART
Autoři
ŠTOHL, R.; STIBOR, K.
Vydáno
18. 11. 2020
Nakladatel
IEEE
Místo
High Tatras, Slovakia
ISBN
978-1-7281-1951-9
Kniha
Proceedings of the 2020 21st International Carpathian Control Conference (ICCC)
Strany od
1
Strany do
7
Strany počet
URL
https://ieeexplore.ieee.org/document/9257284
BibTex
@inproceedings{BUT166122, author="Radek {Štohl} and Karel {Stibor}", title="Predicting Safety Logic Device Solutions via Decision Trees and Rules Algorithms", booktitle="Proceedings of the 2020 21st International Carpathian Control Conference (ICCC)", year="2020", pages="1--7", publisher="IEEE", address="High Tatras, Slovakia", doi="10.1109/ICCC49264.2020.9257284", isbn="978-1-7281-1951-9", url="https://ieeexplore.ieee.org/document/9257284" }