Abstract
The paper is focused on improvement of classification accuracy of decision trees used in the data mining process. Real production data from the paint shop process serve as its basis. The proposal utilizes various approaches for selection of target attribute intervals and classes and key attributes for classification. The decision tree parameters are optimized to obtain the best possible combination. The results are evaluated across multiple decision tree algorithms.
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Acknowledgments
This publication is the result of implementation of the project VEGA 1/0272/18: “Holistic approach of knowledge discovery from production data in compliance with Industry 4.0 concept” supported by the VEGA.
This publication is the result of implementation of the project: “Increase of Power Safety of the Slovak Republic” (ITMS: 26220220077) supported by the Research & Development Operational Programme funded by the ERDF.
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Kebisek, M., Spendla, L., Tanuska, P., Hrcka, L. (2019). Decision Trees Accuracy Improvement for Production Errors Classification. In: Silhavy, R. (eds) Software Engineering and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-319-91186-1_20
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DOI: https://doi.org/10.1007/978-3-319-91186-1_20
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