Rule-based approximation of black-box classifiers for tabular data to generate global and local explanations

• Autorzy: Maszczyk Cezary , Kozielski Michal , Sikora Marek

Identyfikatory

DOI

10.15439/2022F258

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The need to understand the decision bases of artificial intelligence methods is becoming widespread. One method to obtain explanations of machine learning models and their decisions is the approximation of a complex model treated as a black box by an interpretable rule-based model. Such an approach allows detailed and understandable explanations to be generated from the elementary conditions contained in the rule premises. However, there is a lack of research on the evaluation of such an approximation and the influence of the parameters of the rule-based approximator. In this work, a rule-based approximation of complex classifier for tabular data is evaluated. Moreover, it was investigated how selected measures of rule quality affect the approximation. The obtained results show what quality of approximation can be expected and indicate which measure of rule quality is worth using in such application.

Słowa kluczowe

EN

computer science; analytical model; computational modeling; machine learning; data model

PL

informatyka; model analityczny; modelowanie obliczeniowe; uczenie maszynowe; model danych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 89--92
• Opis fizyczny: Bibliogr. 13 poz., tab., wykr.

Twórcy

autor

Maszczyk Cezary

• Doctoral School, Silesian Univeristy of Technology, ul. Akademicka 2A, 44-100 Gliwice, Poland
• Łukasiewicz Research Network – Institute of Innovative Technologies EMAG, ul. Leopolda 31, 40-189 Katowice, Poland

autor

Kozielski Michal

• Department of Computer Networks and Systems, Silesian Univeristy of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

autor

Sikora Marek

• Łukasiewicz Research Network – Institute of Innovative Technologies EMAG, ul. Leopolda 31, 40-189 Katowice, Poland
• Department of Computer Networks and Systems, Silesian Univeristy of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

• 1. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, “A survey of methods for explaining black box models,” ACM Comput. Surv., vol. 51, no. 5, aug 2018. [Online]. Available: https://doi.org/10.1145/3236009
• 2. A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on explainable artificial intelligence (xai),” IEEE Access, vol. 6, pp. 52 138–52 160, 2018.
• 3. C. Molnar, Interpretable Machine Learning, 2nd ed., 2022. [Online]. Available: https://christophm.github.io/interpretable-ml-book
• 4. P. Biecek and T. Burzykowski, Explanatory model analysis: Explore, explain and examine predictive models. Chapman and Hall/CRC, 2021.
• 5. J. W. Grzymala-Busse, Rule Induction. Boston, MA: Springer US, 2005, pp. 277–294. [Online]. Available: https://doi.org/10.1007/0-387-25465-X_13
• 6. E. Pastor and E. Baralis, “Explaining black box models by means of local rules,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, ser. SAC ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 510–517. [Online]. Available: https://doi.org/10.1145/3297280.3297328
• 7. D. Pedreschi, F. Giannotti, R. Guidotti, A. Monreale, S. Ruggieri, and F. Turini, “Meaningful explanations of black box ai decision systems,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 9780–9784.
• 8. M. Setzu, R. Guidotti, A. Monreale, and F. Turini, “Global explanations with local scoring,” in Machine Learning and Knowledge Discovery in Databases, P. Cellier and K. Driessens, Eds. Cham: Springer International Publishing, 2020, pp. 159–171.
• 9. M. Sikora and Ł. Wróbel, “Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms,” International Journal of General Systems, vol. 42, no. 6, pp. 594–613, 2013.
• 10. L. S. Shapley, “A value for n-person games,” Classics in game theory, vol. 69, 1997.
• 11. M. Sikora, “Redefinition of decision rules based on the importance of elementary conditions evaluation,” Fundamenta Informaticae, vol. 123, no. 2, pp. 171–197, 2013.
• 12. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research, vol. 12, pp. 2825–2830, 2011.
• 13. A. Gudyś, M. Sikora, and Łukasz Wróbel, “Rulekit: A comprehensive suite for rule-based learning,” Knowledge-Based Systems, vol. 194, p. 105480, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950705120300046

Uwagi

1. Short article

2. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).