Abstract
In this paper, the kernel algorithm of decision tree is used as a tool, and on the basis of constructing a good data source group for classification, the characteristics of decision tree classification algorithm are analyzed and experimented. This paper introduces a rough set attribute selection metric, which selects attributes for classification from the point of view of improving the accuracy of classification and the purity of sub-databases. Association rule mining is the mining of interesting association rules or relationships between indexes (items) in the database. The paper present extraction method of classification rules in decision tree based on attribute selection metric of rough set.
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Acknowledgments
This paper is supported by Henan key Laboratory for Big Data Processing & Analytics of Electronic Commerce, and also supported by the science and technology research major project of Henan province Education Department (17B520026).
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Wang, L., Xu, H. (2020). Extraction Method of Classification Rules in Decision Tree Based on Attribute Selection Metric of Rough Set. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_115
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DOI: https://doi.org/10.1007/978-3-030-25128-4_115
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