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QLC: A Quantum-Logic-inspired Classifier

  • Besides a good prediction a classifier is to give an explanation how the input data is related to the classification result. There is a general agreement that logic expressions provide a better explanation than other methods like SVM, logistic regression, and neural networks. However, a classifier based on Boolean logic needs to map continuous data to Boolean values which can cause a loss of information. In contrast, we design a quantum-logic-inspired classifier where continuous data are directly processed and the laws of the Boolean algebra are maintained. As a result from our approach we obtain a CQQL condition which provides good insights into the relation of input features to the class decision. Furthermore, our experiment shows a good prediction accuracy.

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Metadaten
Author: Ingo SchmittORCiDGND
DOI:https://doi.org/10.1145/3548785.3548790
ISBN:9781450397094
Title of the source (English):IDEAS '22: Proceedings of the 26th International Database Engineered Applications Symposium
Publisher:Association of Computing Machinery
Place of publication:New York
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2022
First Page:120
Last Page:127
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Datenbank- und Informationssysteme
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