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The Digitization of Society – Case of Specific Chosen State Alliance of Four Central European States Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Decision Trees Accuracy Improvement for Production Errors Classification

Authors : Michal Kebisek, Lukas Spendla, Pavol Tanuska, Lukas Hrcka

Published in: Software Engineering and Algorithms in Intelligent Systems

Publisher: Springer International Publishing

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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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Metadata
Title
Decision Trees Accuracy Improvement for Production Errors Classification
Authors
Michal Kebisek
Lukas Spendla
Pavol Tanuska
Lukas Hrcka
Copyright Year
2019
Book
Software Engineering and Algorithms in Intelligent Systems

Print ISBN: 978-3-319-91185-4

Electronic ISBN: 978-3-319-91186-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-91186-1_20

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