Abstract
Decision rules are widely used due to their interpretability, efficiency, and stability in various applications, especially for financial tasks, such as fraud detection and loan assessment. In many scenarios, it is highly demanded to generate decision rules under some specific constraints. However, the performance, efficiency, and adaptivity of previous methods, which take no consideration of these constraints, is far from satisfactory in these scenarios, especially when the constraints are relatively tight. In this paper, to deal with this problem, we propose a constraint-adaptive rule mining algorithm named CARM (Constraint Adaptive Rule Mining), which is a novel decision tree based model. To provide a practical balance between purity and constraint fitness when building the trees, an adaptive criterion is designed and applied to better meet the constraints. Besides, a rule extraction and pruning process is applied to satisfy the constraints and further alleviate the overfitting problem. In addition, to improve the coverage, an iterative covering framework is proposed in this paper. Experiments on both public and business data sets show that the proposed method is able to achieve better performance, competitive efficiency, as well as low rule complexity when comparing with other methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press (1996)
Clark, P., Niblett, T.: The CN2 induction algorithm. Mach. Learn. 3, 261–283 (1989). https://doi.org/10.1023/A:1022641700528
Dash, S., Günlük, O., Wei, D.: Boolean decision rules via column generation. In: NIPS, pp. 4660–4670 (2018)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: ICML, pp. 144–151. Morgan Kaufmann (1998)
Gao, Q., Xu, D.: An empirical study on the application of the evidential reasoning rule to decision making in financial investment. Knowl.-Based Syst. 164, 226–234 (2019)
Hajek, P.: Interpretable fuzzy rule-based systems for detecting financial statement fraud. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 559, pp. 425–436. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_36
Bayardo Jr., R.J., Agrawal, R., Gunopulos, D.: Constraint-based rule mining in large, dense databases. In: ICDE, pp. 188–197 (1999)
Letham, B., Rudin, C., McCormick, T.H., Madigan, D.: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350–1371 (2015)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD, pp. 80–86 (1998)
Marchand, M., Sokolova, M.: Learning with decision lists of data-dependent features. JMLR 6, 427–451 (2005)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986). https://doi.org/10.1007/BF00116251
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)
Rudin, C., Letham, B., Madigan, D.: Learning theory analysis for association rules and sequential event prediction. JMLR 14(1), 3441–3492 (2013)
Wang, T., Rudin, C., Doshi-Velez, F., Liu, Y., Klampfl, E., MacNeille, P.: A Bayesian framework for learning rule sets for interpretable classification. JMLR 18, 70:1–70:37 (2017)
Yang, H., Rudin, C., Seltzer, M.: Scalable Bayesian rule lists. In: ICML, vol. 70, pp. 3921–3930. PMLR (2017)
Zhang, Y.L., Li, L.: Interpretable MTL from heterogeneous domains using boosted tree. In: CIKM, pp. 2053–2056 (2019)
Zhang, Y., et al.: Distributed deep forest and its application to automatic detection of cash-out fraud. TIST 10(5), 55:1–55:19 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, M. et al. (2021). Constraint-Adaptive Rule Mining in Large Databases. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-73200-4_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73199-1
Online ISBN: 978-3-030-73200-4
eBook Packages: Computer ScienceComputer Science (R0)