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An improved decision tree algorithm based on boundary mixed attribute dependency

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Abstract

As an effective extension of rough set theory, the variable precision neighborhood rough set model has been applied to the attribute dependency-based improvement of decision tree algorithm of the solution concerning continuous data. However, the boundary region, as an effective description of the uncertainty of knowledge, has not been taken into account in the existing algorithms. In this paper, we define a novel decision rule based on boundary region and attribute dependency, and construct a decision tree algorithm via this decision rule. First, we define a measure called boundary coefficient based on the boundary region, which can be used for comparative quantitative analysis. Second, we define the boundary mixed attribute dependency by combining the boundary coefficient and the attribute dependency, which can consider both the boundary case of the target set and the attribute dependency. Finally, a novel decision tree algorithm is proposed by using the boundary mixed attribute dependency as the decision rule. The experimental results show that with a slight increase in leaf nodes, the total running time decreases and the maximum accuracy increases to 0.9518, which indicates the effectiveness of the proposed algorithm.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant Nos. 62166001, Graduate Innovation Funding Program of Gannan Normal University, China under Grant No. YCX22A025.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, Jiangxi, China

    Bowen Lin & Caihui Liu

  2. Department of Computer Science and Technology, Tongji University, 201804, Shanghai, China

    Duoqian Miao

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  1. Bowen Lin
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  2. Caihui Liu
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  3. Duoqian Miao
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Contributions

Bowen Lin: Conceptualization, Methodology, Writing - original draft, Software, Validation, Data-curation. Caihui Liu: Conceptualization, Methodology, Writing - review & editing, Validation, Supervision. Duoqian Miao: Writing - review & editing.

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Correspondence to Caihui Liu.

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Lin, B., Liu, C. & Miao, D. An improved decision tree algorithm based on boundary mixed attribute dependency. Appl Intell 54, 2136–2153 (2024). https://doi.org/10.1007/s10489-023-05238-4

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