Abstract
A decision tree [1] is a data mining tool commonly used in data classification tasks. Apart from providing satisfactorily high accuracies, the results produced by decision trees are easily interpretable. A decision tree, in fact, divides attribute values space X into disjoint subspaces. The most common decision tree induction algorithms for static data sets are the ID3 algorithm [2], the C4.5 algorithm [3, 4], and the CART algorithm [5].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pinder, J.P.: Decision trees. In: Pinder, J.P. (ed.) Introduction to Business Analytics using Simulation, pp. 47–69. Academic Press, Boston (2017)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Yang, Y., Chen, W.: Taiga: performance optimization of the C4.5 decision tree construction algorithm. Tsinghua Sci. Technol. 21, 415–425 (2016)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)
Lomax, S., Vadera, S.: A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework. Comput. J. 60, 941–956 (2017)
Li, J., Ma, S., Le, T., Liu, L., Liu, J.: Causal decision trees. IEEE Trans. Knowl. Data Eng. 29, 257–271 (2017)
Pei, S., Hu, Q.: Partially monotonic decision trees. Inf. Sci. 424, 104–117 (2018)
Wang, L., Li, Q., Yu, Y., Liu, J.: Region compatibility based stability assessment for decision trees. Expert. Syst. Appl. 105, 112–128 (2018)
Nguyen, K., Tran, D., Ma, W., Sharma, D.: Decision tree algorithms for image data type identification. Log. J. IGPL 25, 67–82 (2017)
Segatori, A., Marcelloni, F., Pedrycz, W.: On distributed fuzzy decision trees for big data. IEEE Trans. Fuzzy Syst. 26, 174–192 (2018)
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58, 13–30 (1963)
From, S.G., Swift, A.W.: A refinement of Hoeffding’s inequality. J. Stat. Comput. Simul. 83(5), 977–983 (2013)
Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)
Gama, J.: Accurate decision trees for mining high-speed data streams. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528. ACM Press (2003)
Kirkby, R.: Improving Hoeffding trees. Ph.D. thesis, University of Waikato (2007)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rutkowski, L., Jaworski, M., Duda, P. (2020). Decision Trees in Data Stream Mining. In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-13962-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13961-2
Online ISBN: 978-3-030-13962-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)