Abstract
Ordinary decision tree classifiers are used to classify data with single-valued attributes and single-class labels. This paper develops a new decision tree classifier SSC for multi-valued and multi-labeled data, on the basis of the algorithm MMDT, improves on the core formula for measuring the similarity of label-sets, which is the essential index in determining the goodness of splitting attributes, and proposes a new approach of measuring similarity considering both same and consistent features of label-sets, and together with a dynamic approach of adjusting the calculation proportion of the two features according to current data set. SSC makes the similarity of label-sets measured more comprehensive and accurate. The empirical results prove that SSC indeed improves the accuracy of MMDT, and has better classification efficiency.
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
Preview
Unable to display preview. Download preview PDF.
References
Han, J., Kamber, M.: Data Mining Concept and Technology. [M]. China Machine Press, Peking (2001)
Shafer, J.C., Agrawal, R., Mehta, M.: SPRINT: A scalable parallel classifier for data mining. In: Proceedings of the 22nd International Conference on Very Large Databases, Mumbai (Bombay), India, pp. 544–555 (1996)
Chen, Y., Hsu, C., Chou, S.: Constructing a multi-valued and multi-labeled decision tree. Expert Systems with Applications 25(2), 199–209 (2003)
Chou, S., Hsu, C.: MMDT: a multi-valued and multi-labeled decision tree classifier for data mining. Expert Systems with Applications 28(2), 799–812 (2005)
Mantaras, R.L.D.: A distance-based attribute selection measure for decision tree induction. Machine Learning 6(1), 81–92 (1991)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. China Machine Press (2003)
Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., Swami, A.: An interval classifier for database mining applications. In: Proceedings of the 18th International Conference on Very Large Databases, Vancouver, BC, pp. 560–573 (1992)
Ruggieri, S.: Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering 14(2), 438–444 (2002)
Wang, H., Zaniolo, C.: CMP: A fast decision tree classifier using multivariate predictions. In: Proceedings of the 16th International Conference on Data Engineering, pp. 449–460 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Zhao, R., Chen, J., Xiang, Y. (2006). Research on Multi-valued and Multi-labeled Decision Trees. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_27
Download citation
DOI: https://doi.org/10.1007/11811305_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)