Abstract
A decision tree with verifying cuts, called V-tree, uses additional knowledge encoded in many attributes to classify new objects. The purpose of the verifying cuts is to confirm the correctness of the partitioning of tree nodes based on the (semi)-optimal cut determined by a greedy approach. The confirmation may be relevant because for some new objects there are discrepancies in the class prediction on the basis of the individual verifying cuts. In this paper we present a new method for resolving conflicts between cuts assigned to node. The method uses an additional local discretization classifier in each node where there is a conflict between the cuts. The paper includes the results of experiments performed on data sets from a biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, well known from literature and called here C-tree, as well as a V-tree with previous simple conflict resolution method. Our new approach outperforms the C-tree, although it does not produce better results than V-tree with simple method of conflict resolution for the surveyed data sets. However, the proposed method is a step toward a deeper analysis of conflicts between rules.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Dydo, L., Rzasa, W., Skowron, A.: A classifier based on a decision tree with verifying cuts. Fundam. Inform. 143(1–2), 1–18 (2016)
Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P.W., Sokolowska, B.: Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the Federated Conference on Computer Science and Information Systems, pp. 279–286. IEEE Xplore - digital library (2012)
Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P.W., Sokolowska, B.: Prediction of coronary arteriosclerosis in stable coronary heart disease. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012. CCIS, vol. 298, pp. 550–559. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31715-6_58
Bazan, J.G., Buregwa-Czuma, S.: A domain knowledge as a tool for improving classifiers. Fundam. Inform. 127(1–4), 495–511 (2013)
Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problems. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information. Systems Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Springer/Physica, Heidelberg (2000). doi:10.1007/978-3-7908-1840-6_3
Bazan, J.G., Szczuka, M.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005). doi:10.1007/11427834_2
Buregwa-Czuma, S., Bazan, J.G., Zareba, L., Bazan-Socha, S., Pardel, P., Sokolowska, B., Dydo, L.: The method for describing changes in the perception of stenosis in blood vessels caused by an additional drug. Fundam. Inform. 147, 193–207 (2016)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
The Elements of Statistical Learning Repository: http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/
Kent Ridge Biomedical Dataset Repository: http://datam.i2r.a-star.edu.sg/datasets/krbd/
Nguyen, H.S.: Approximate boolean reasoning: foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334–506. Springer, Heidelberg (2006). doi:10.1007/11847465_16
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177, 3–27 (2007)
UC Irvine Machine Learning Repository: http://archive.ics.uci.edu/ml/
Acknowledgement
This work was partially supported by two following grants of the Polish National Science Centre: DEC-2013/09/B/ST6/01568, DEC-2013/09/B/NZ5/00758, and also by the Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge of University of Rzeszów, Poland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Buregwa-Czuma, S., Bazan, J.G., Bazan-Socha, S., Rzasa, W., Dydo, L., Skowron, A. (2017). Resolving the Conflicts Between Cuts in a Decision Tree with Verifying Cuts. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-60840-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60839-6
Online ISBN: 978-3-319-60840-2
eBook Packages: Computer ScienceComputer Science (R0)