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.
Author: | Ingo SchmittORCiDGND |
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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 |