Abstract
The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of the most popular interpretable models that are classification rules. This work aims to propose an incremental model for learning interpretable and balanced rules based on MaxSAT, called IMLIB. This new model was based on two other approaches, one based on SAT and the other on MaxSAT. The one based on SAT limits the size of each generated rule, making it possible to balance them. We suggest that such a set of rules seem more natural to be understood compared to a mixture of large and small rules. The approach based on MaxSAT, called IMLI, presents a technique to increase performance that involves learning a set of rules by incrementally applying the model in a dataset. Finally, IMLIB and IMLI are compared using diverse databases. IMLIB obtained results comparable to IMLI in terms of accuracy, generating more balanced rules with smaller sizes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Source code of IMLIB and the implementation of the tests performed can be found at the link: https://github.com/cacajr/decision_set_models.
References
Biran, O., Cotton, C.: Explanation and justification in machine learning: a survey. In: IJCAI-17 Workshop on Explainable AI (XAI), vol. 8, pp. 8–13 (2017)
Carleo, G., et al.: Machine learning and the physical sciences. Rev. Mod. Phys. 91(4), 045002 (2019)
Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3(11), e745–e750 (2021)
Ghosh, B., Malioutov, D., Meel, K.S.: Efficient learning of interpretable classification rules. J. Artif. Intell. Res. 74, 1823–1863 (2022)
Ghosh, B., Meel, K.S.: IMLI: an incremental framework for MaxSAT-based learning of interpretable classification rules. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 203–210 (2019)
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI-explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)
Huang, H.Y., et al.: Power of data in quantum machine learning. Nat. Commun. 12(1), 2631 (2021)
Ignatiev, A., Marques-Silva, J., Narodytska, N., Stuckey, P.J.: Reasoning-based learning of interpretable ML models. In: IJCAI, pp. 4458–4465 (2021)
Ignatiev, A., Morgado, A., Marques-Silva, J.: RC2: an efficient MaxSAT solver. J. Satisfiability Boolean Modeling Comput. 11(1), 53–64 (2019)
Ignatiev, A., Pereira, F., Narodytska, N., Marques-Silva, J.: A SAT-based approach to learn explainable decision sets. In: Galmiche, D., Schulz, S., Sebastiani, R. (eds.) IJCAR 2018. LNCS (LNAI), vol. 10900, pp. 627–645. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94205-6_41
Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electron. Mark. 31(3), 685–695 (2021)
Jiménez-Luna, J., Grisoni, F., Schneider, G.: Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2(10), 573–584 (2020)
Kwekha-Rashid, A.S., Abduljabbar, H.N., Alhayani, B.: Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl. Nanosci. 1–13 (2021)
Lakkaraju, H., Bach, S.H., Leskovec, J.: Interpretable decision sets: a joint framework for description and prediction. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1675–1684 (2016)
Maliotov, D., Meel, K.S.: MLIC: a MaxSAT-based framework for learning interpretable classification rules. In: Hooker, J. (ed.) CP 2018. LNCS, vol. 11008, pp. 312–327. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98334-9_21
Mita, G., Papotti, P., Filippone, M., Michiardi, P.: LIBRE: learning interpretable Boolean rule ensembles. In: AISTATS, pp. 245–255. PMLR (2020)
Rocha, T.A., Martins, A.T.: Synthesis of quantifier-free first-order sentences from noisy samples of strings. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pp. 12–17. IEEE (2019)
Rocha, T.A., Martins, A.T., Ferreira, F.M.: Synthesis of a DNF formula from a sample of strings using Ehrenfeucht-Fraïssé games. Theoret. Comput. Sci. 805, 109–126 (2020)
Sharma, A., Jain, A., Gupta, P., Chowdary, V.: Machine learning applications for precision agriculture: a comprehensive review. IEEE Access 9, 4843–4873 (2020)
Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4793–4813 (2020)
Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Inf. Fusion 76, 89–106 (2021)
Yan, L., et al.: An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2(5), 283–288 (2020)
Yu, J., Ignatiev, A., Stuckey, P.J., Le Bodic, P.: Computing optimal decision sets with SAT. In: Simonis, H. (ed.) CP 2020. LNCS, vol. 12333, pp. 952–970. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58475-7_55
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ferreira Júnior, A.C.S., Rocha, T.A. (2023). An Incremental MaxSAT-Based Model to Learn Interpretable and Balanced Classification Rules. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14195. Springer, Cham. https://doi.org/10.1007/978-3-031-45368-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-45368-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45367-0
Online ISBN: 978-3-031-45368-7
eBook Packages: Computer ScienceComputer Science (R0)