Abstract
Decision trees are one of the most popular structures for decision-making and the representation of a set of rules. However, when a rule set is represented as a decision tree, some quirks in its structure may negatively affect its performance. For example, duplicate sub-trees and rule filters, that need to be evaluated more than once, could negatively affect the efficiency. This paper presents a novel algorithm based on Shannon’s expansion, which guarantees that the same rule filter is not evaluated more than once, even if repeated in other rules. This fact increases efficiency during the evaluation process using the induced decision tree. Experiments demonstrated the viability of the proposed algorithm in processing-intensive scenarios, such as in intrusion detection and data stream analysis.
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Acknowledgement
This research was supported by the Universidad Iberoamericana (Ibero) and the Institute of Applied Research and Technology (InIAT) by the project “Detection of phishing attacks in electronic messages using Artificial Intelligence techniques.”
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Herrera-Semenets, V., Bustio-Martínez, L., Hernández-León, R., van den Berg, J. (2024). A Decision Tree Induction Algorithm for Efficient Rule Evaluation Using Shannon’s Expansion. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_18
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