Abstract
This study proposes the BBQ-Tree, a new logic-based classifier that combines the two concepts of classical Decision Trees and Quantum-Logic Decision Trees into a generalized model. It thus creates a method that has the power to solve classification problems that incorporate both curved and linear decision boundaries, with a particular focus on interpretability. In addition to the model itself, ways for its efficient training are discussed. Our experimental evaluation demonstrates that our approach is able to produce models that remain compact and provide good insights over trends in data while maintaining an accuracy not worse than Decision Trees alone.
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Stahl, A., Schmitt, I. (2024). BBQ-Tree – A Decision Tree with Boolean and Quantum Logic Decisions. In: Tekli, J., Gamper, J., Chbeir, R., Manolopoulos, Y. (eds) Advances in Databases and Information Systems. ADBIS 2024. Lecture Notes in Computer Science, vol 14918. Springer, Cham. https://doi.org/10.1007/978-3-031-70626-4_14
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