Abstract
Aiming at the problems that traditional data mining methods ignore inconsistent data, and general decision tree learning algorithms lack of theoretical support for the classification of inconsistent nodes. The three-way decision is introduced to decision tree learning algorithms,and the decision tree learning method based on three-way decisions is proposed. Firstly, the proportion of positive objects in node is used to compute the conditional probability of the three-way decision of node. Secondly, the nodes in decision tree arepartitioned to generate the three-way decision tree. The merger and pruning rules of the three-way decision tree are derived to convert the three-way decision tree into two-way decision tree by considering the information around nodes. Finally, an exampleisimplemented. The results show that the proposed method reserves inconsistent information, partitions inconsistent nodes by minimizing the overall risk, not only generates decision tree with cost-sensitivity, but also makes the partition of inconsistent nodes more explicable. Besides, the proposed method reduces the overfitting to some extent and the computation problem of conditional probability of three-way decisions is resolved.
This is a preview of subscription content, log in via an institution to check access.
References
Qian, W.B., Yang, B.R., Xu, Z.Y., Xie, Y.H.: Rule extraction algorithm based on discernibility matrix in inconsistent decision table. Comput. Sci. 40(6), 215–218 (2013)
Meng, Z.Q., Zhou, S.Q.: Research method of generalized decision rule acquisition based on GrC in inconsistent decision systems. Comput. Sci. 39(1), 198–202 (2012)
Diogo, R., Ferreira, E.V.: Using logical decision trees to discover the cause of process delays from event logs. Comput. Ind. 70, 194–207 (2015)
Hong, K.S., Melanie, P.O., Ye, C.K.: Sparse alternating decision tree. Pattern Recogn. Lett. 60, 57–64 (2015)
Mistikoglu, G., Gerek, I.H., Erdis, E., Mumtaz Usmen, P.E., Cakan, H., Kazan, E.E.: Decision tree analysis of construction fall accidents involving roofers. Expert Syst. Appl. 42(4), 2256–2263 (2015)
Chen, J.K., Wang, X.Z., Gao, X.H.: Improved ordinal decisions trees algorithms based on rank entropy. Pattern Recogn. Artif. Intell. 27(2), 134–140 (2014)
Ruan, X.H., Huang, X.M., Yuan, D.R., Duan, Q.L.: Classification algorithm based on heterogeneous cost-sensitive decision tree. Comput. Sci. 40(11A), 140–142 (2013)
Jia, X.Y., Shang, L., Zhou, X.Z., Liang, J.Y., Miao, D.Q., Wang, G.Y., Li, T.R., Zhang, Y.P.: The Theory and Application of Three-way Decision. Nanjing University Press, Nanjing (2012)
Liu, D., Li, T., Liang, D.: A new discriminant analysis approach under decision-theoretic rough sets. In: Yao, J.T., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 476–485. Springer, Heidelberg (2011)
Yao, Y., Zhou, B.: Naive bayesian rough sets. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 719–726. Springer, Heidelberg (2010)
Jia, X.Y., Tang, Z.M., Liao, W.H., Shang, L.: On an optimization representation of decision-theoretic rough set model. Int. J. Approximate Reasoning 55(1), 156–166 (2014)
Li, H.X., Zhou, X.Z., Zhao, J.B.: Non-nonotonic attribute reduction indecision-theoretic rough sets. Fundamenta Informaticae 126(4), 415–432 (2013)
Liu, D., Li, T.R., Liang, D.C.: Incorporating logistic regression to decisiontheoretic rough sets for classifications. Int. J. Approximate Reasoning 55(1), 197–210 (2014)
Li, H.: Statistical Learning Method. Tsinghua University Press, Beijing (2012)
Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)
Liu, D., Li, T.R., Liang, D.C.: Incorporating logistic regression to decision-theoretic rough sets for classifications. Int. J. Approximate Reasoning 55, 197–210 (2014)
Liu, D., Yao, Y.Y., Li, T.R.: Three-way decision-theoretic rough sets. Comput. Sci. 38(1), 246–250 (2011)
Yao, Y.: Three-way decision: an interpretation of rules in rough set theory. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 642–649. Springer, Heidelberg (2009)
Liu, D., Li, T.R., Liang, D.C.: Incorporating logistic regression to decision-theoretic rough sets for classifications. Int. J. Approximate Reasoning 55(1), 197–210 (2013)
Liu, D., Li, T.R., Li, H.X.: Rough set theory: a three-way decisions perspective. J. Nanjing Univ. (Natural Sciences) 49(5), 574–581 (2013)
Acknowledgements
The authors wish to thank the anonymous reviewers and Editor-in-Chief for their valuable comments and hard work. This work was supported by the National Natural Science Foundation of China (Nos. 61370169, 61402153,60873104), the Key Project of Science and Technology Department of Henan Province (Nos. 142102210056, 112102210194), the Science and Technology Research Key Project of Educational Department of Henan Province (Nos.12A520027, 13A520529), the Key Project of Science and Technology of Xinxiang Government (No. ZG13004), the Education Fund for Youth Key Teachers of Henan Normal University, and the 2014 Henan Normal University Youth Science Fund(No. 2014QK28).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, Y., Xu, J., Sun, L., Du, L. (2015). Decisions Tree Learning Method Based on Three-Way Decisions. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-25783-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25782-2
Online ISBN: 978-3-319-25783-9
eBook Packages: Computer ScienceComputer Science (R0)