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A Decision Tree Induction Algorithm for Efficient Rule Evaluation Using Shannon’s Expansion

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Advances in Computational Intelligence (MICAI 2023)

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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References

  1. Abdelhalim, A., Traore, I., Nakkabi, Y.: Creating decision trees from rules using rbdt-1. Comput. Intell. 32(2), 216–239 (2016)

    Article  MathSciNet  Google Scholar 

  2. Ahmim, A., Maglaras, L., Ferrag, M.A., Derdour, M., Janicke, H.: A novel hierarchical intrusion detection system based on decision tree and rules-based models. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 228–233. IEEE (2019)

    Google Scholar 

  3. Al-Daweri, M.S., et al.: An analysis of the kdd99 and unsw-nb15 datasets for the intrusion detection system. Symmetry 12(10), 1666 (2020)

    Google Scholar 

  4. Charbuty, B., Abdulazeez, A.: Classification based on decision tree algorithm for machine learning. J. Appl. Sci. Technol. Trends 2(01), 20–28 (2021)

    Article  Google Scholar 

  5. Herrera-Semenets, V., Pérez-García, O.A., Gago-Alonso, A., Hernández-León, R.: Classification rule-based models for malicious activity detection. Intell. Data Anal. 21(5), 1141–1154 (2017)

    Article  Google Scholar 

  6. Herrera-Semenets, V., Pérez-García, O.A., Hernández-León, R., van den Berg, J., Doerr, C.: A data reduction strategy and its application on scan and backscatter detection using rule-based classifiers. Exp. Syst. Appl. 95, 272–279 (2018)

    Article  Google Scholar 

  7. Jaïdi, F.: A novel concept of firewall-filtering service based on rules trust-risk assessment. In: Madureira, A.M., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds.) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018), pp. 298–307. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17065-3_30

  8. Ligêza, A.: Logical Foundations for Rule-Based Systems. Springer, Heidelberg (2006)

    Google Scholar 

  9. Schudel, G.: Bandwidth, packets per second, and other network performance metrics. Abgerufen am 10, 2010 (2010)

    Google Scholar 

  10. Soufi, M.D., Samad-Soltani, T., Vahdati, S.S., Rezaei-Hachesu, P.: Decision support system for triage management: a hybrid approach using rule-based reasoning and fuzzy logic. Int. J. Med. Informatics 114, 35–44 (2018)

    Article  Google Scholar 

  11. Yates, D., Islam, M.Z., Gao, J.: SPAARC: a fast decision tree algorithm. In: Islam, R., et al. (eds.) AusDM 2018. CCIS, vol. 996, pp. 43–55. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6661-1_4

  12. Zhang, G., Gionis, A.: Diverse rule sets. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD20), pp. 1532–1541. Association for Computing Machinery, New York (2020)

    Google Scholar 

  13. Zhang, J., Yang, G., Hung, W.N., Zhang, Y., Wu, J.: An efficient NPN Boolean matching algorithm based on structural signature and Shannon expansion. Clust. Comput. 22(3), 7491–7506 (2019)

    Article  Google Scholar 

  14. Zhao, J., Wu, M., Zhou, L., Wang, X., Jia, J.: Cognitive psychology-based artificial intelligence review. Front. Neurosci. 16, 1024316 (2022)

    Article  Google Scholar 

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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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Correspondence to Lázaro Bustio-Martínez .

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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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  • DOI: https://doi.org/10.1007/978-3-031-47765-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47764-5

  • Online ISBN: 978-3-031-47765-2

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