Abstract
The tree based method is a conventional statistical method that involves constructing a tree structure for a classification model through recursively splitting a dataset by explanatory variables to minimize some impurity criteria for the response variable. This tree structure induces many if-then rules with product forms. In this paper, we study a basic tree based approach — the classification and regression trees (CART) method — based on a simulation model for data generation and verification for induced rules. We compare CART with the statistical test rule induction method (STRIM) to clarify its performance and problems. We also apply both methods to a real-world dataset and consider their performances based on the simulation results.
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References
Matsubayashi, T., Kato, Y., Saeki, T.: A new rule induction method from a decision table using a statistical test. In: Li, T., et al. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 81–90. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31900-6_11
Kato, Y., Saeki, T., Mizuno, S.: Studies on the necessary data size for rule induction by STRIM. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 213–220. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41299-8_20
Kato, Y., Saeki, T., Mizuno, S.: Considerations on rule induction procedures by STRIM and their relationship to VPRS. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS (LNAI), vol. 8537, pp. 198–208. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08729-0_19
Saeki, T., Kato, Y., Mizuno, S.: Studies of rule induction by STRIM from the decision table with contaminated attribute values from missing data and noise – in the case of critical dataset size–. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 19(6), 1244–1249 (2015)
Kato, Y., Saeki, T., Mizuno, S.: Proposal of a statistical test rule induction method by use of the decision table. Appl. Soft Comput. 28, 160–166 (2015)
Kato, Y., Saeki, T., Mizuno, S.: Proposal for a statistical reduct method for decision tables. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS (LNAI), vol. 9436, pp. 140–152. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25754-9_13
Kitazaki, Y., Saeki, T., Kato, Y.: Performance comparison to a classification problem by the second method of quantification and STRIM. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 406–415. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47160-0_37
Fei, J., Saeki, T., Kato, Y.: Proposal for a new reduct method for decision tables and an improved STRIM. In: Tan, Y., Takagi, H., Shi, Y. (eds.) DMBD 2017. LNCS, vol. 10387, pp. 366–378. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61845-6_37
Kato, Y., Itsuno, T., Saeki, T.: Proposal of dominance-based rough set approach by STRIM and its applied example. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10313, pp. 418–431. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_35
Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11(5), 341–356 (1982)
Skowron, A., Rauser, C.M.: The discernibility matrix and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Application and Advances of Rough Set Theory, vol. 11, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992). https://doi.org/10.1007/978-94-015-7975-9_21
Grzymala-Busse, J.W.: LERS – a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, vol. 11, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992). https://doi.org/10.1007/978-94-015-7975-9_1
Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)
Brieman, L., Frieman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)
Frieman, J.H.: Greedy function approximation: gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Brieman, L.: Bagging predictions. Mach. Learn. 26(2), 123–140 (1996)
Brieman, L.: Random forests. Mach. Learn. 45(1), 5–23 (2001)
Zheng, Z., Wang, G., Wu, Y.: A rough set and rule tree based incremental knowledge acquisition algorithm. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 122–129. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-39205-X_16
Sikder, I.U., Munakata, T.: Application of rough set and decision tree for characterization of premonitory factors of low seismic activity. Expert Syst. Appl. 36, 102–110 (2009)
Buregwa-Czuma, S., Bazan, J.G., Bazan-Socha, S., Rzasa, W., Dydo, L., Skowron, A.: Resolving the conflicts between cuts in a decision tree with verifying cuts. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 403–422. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60840-2_30
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We truly thank Rakuten Inc. for presenting Rakuten Travel dataset [19].
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Kato, Y., Kawaguchi, S., Saeki, T. (2018). Studies on CART’s Performance in Rule Induction and Comparisons by STRIM. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_12
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