Bidirectional Action Rule Learning

  • Paweł Matyszok
  • Łukasz WróbelEmail author
  • Marek Sikora
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 935)


Action rules specify recommendations which should be followed in order to transfer objects to the desired decision class. In this paper influence of employing information contained in source and target class examples in sequential covering based action rule induction method is examined. Results show that using source class for guiding the induction process produces best results.


Action rules Classification Data mining 



This work was partially supported by Polish National Centre for Research and Development (NCBiR) within the programme Prevention and Treatment of Civilization Diseases – STRATEGMED III, grant number STRATEGMED3/ 304586/5/NCBR/2017 (PersonALL). A part of the work was carried out within the statutory research project of the Institute of Informatics, BK-213/RAU2/2018.


  1. 1.
    Almardini, M., et al.: Reduction of readmissions to hospitals based on actionable knowledge discovery and personalization. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015-2016. CCIS, vol. 613, pp. 39–55. Springer, Cham (2016). Scholar
  2. 2.
    Blachnik, M.: Instance selection for classifier performance estimation in meta learning. Entropy 19(11), 583 (2017)CrossRefGoogle Scholar
  3. 3.
    Dardzinska, A.: Action Rules Mining, vol. 468. Springer, Cham (2012)zbMATHGoogle Scholar
  4. 4.
    Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer, Cham (2012)CrossRefGoogle Scholar
  5. 5.
    Greco, S., Matarazzo, B., Pappalardo, N., Słowinski, R.: Measuring expected effects of interventions based on decision rules. J. Exp. Theor. Artif. Intell. 17(1–2), 103–118 (2005)CrossRefGoogle Scholar
  6. 6.
    Hajja, A., Raś, Z.W., Wieczorkowska, A.A.: Hierarchical object-driven action rules. J. Intell. Inf. Syst. 42(2), 207–232 (2014)CrossRefGoogle Scholar
  7. 7.
    He, Z., Xu, X., Deng, S.: Data mining for actionable knowledge: a survey (2005). arXiv preprint arXiv:cs/0501079
  8. 8.
    Im, S., Raś, Z.W.: Action rule extraction from a decision table: ARED. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 160–168. Springer, Heidelberg (2008). Scholar
  9. 9.
    Pawlak, Z.: Information systems theoretical foundations. Inf. Syst. 6(3), 205–218 (1981)CrossRefGoogle Scholar
  10. 10.
    Raś, Z.W., Tzacheva, A.A., Tsay, L.S., Giirdal, O.: Mining for interesting action rules. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 187–193. IEEE (2005)Google Scholar
  11. 11.
    Ras, Z.W., Wieczorkowska, A.: Action-rules: how to increase profit of a company. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 587–592. Springer, Heidelberg (2000). Scholar
  12. 12.
    Słowiński, R., Greco, S.: Measuring attractiveness of rules from the viewpoint of knowledge representation, prediction and efficiency of intervention. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 11–22. Springer, Heidelberg (2005). Scholar
  13. 13.
    Stańczyk, U., Zielosko, B.: On combining discretisation parameters and attribute ranking for selection of decision rules. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10313, pp. 329–349. Springer, Cham (2017). Scholar
  14. 14.
    Touati, H., Raś, Z.W., Studnicki, J., Wieczorkowska, A.A.: Mining surgical meta-actions effects with variable diagnoses’ number. In: Andreasen, T., Christiansen, H., Cubero, J.-C., Raś, Z.W. (eds.) ISMIS 2014. LNCS (LNAI), vol. 8502, pp. 254–263. Springer, Cham (2014). Scholar
  15. 15.
    Trépos, R., Salleb-Aouissi, A., Cordier, M.O., Masson, V., Gascuel-Odoux, C.: Building actions from classification rules. Knowl. Inf. Syst. 34(2), 267–298 (2013)CrossRefGoogle Scholar
  16. 16.
    Wang, K., Jiang, Y., Tuzhilin, A.: Mining actionable patterns by role models. In: 22nd International Conference on Data Engineering, pp. 16–16. IEEE (2006)Google Scholar
  17. 17.
    Wróbel, Ł., Sikora, M., Michalak, M.: Rule quality measures settings in classification, regression and survival rule induction-an empirical approach. Fundam. Inform. 149(4), 419–449 (2016)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhu, H.M., Huang, W.D., Zheng, H.S.: Method for discovering actionable rule. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery, vol. 1, pp. 397–401. IEEE (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Paweł Matyszok
    • 1
  • Łukasz Wróbel
    • 1
    • 2
    Email author
  • Marek Sikora
    • 1
    • 2
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  2. 2.Institute of Innovative Technologies EMAGKatowicePoland

Personalised recommendations