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QLDT: A Decision Tree Based on Quantum Logic

  • Besides a good prediction a classifier is to give an explanation how input data is related to the classification result. Decision trees are very popular classifiers and provide a good trade-off between accuracy and explainability for many scenarios. Its split decisions correspond to Boolean conditions on single attributes. In cases when for a class decision several attribute values interact gradually with each other, Boolean-logic-based decision trees are not appropriate. For such cases we propose a quantum-logic inspired decision tree (QLDT) which is based on sums and products on normalized attribute values. In contrast to decision trees based on fuzzy logic a QLDT obeys the rules of the Boolean algebra.

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Metadaten
Author: Ingo SchmittORCiDGND
DOI:https://doi.org/10.1007/978-3-031-15743-1_28
ISBN:978-3-031-15743-1
ISBN:978-3-031-15742-4
Title of the source (English):New Trends in Database and Information Systems. ADBIS 2022
Publisher:Springer
Place of publication:Switzerland
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2022
Tag:Decission tree; Interpretable AI; Quantum Logic
First Page:299
Last Page:308
Series ; volume number:Communications in Computer and Information Science ; 1652
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Datenbank- und Informationssysteme
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