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An improved ID3 algorithm based on variable precision neighborhood rough sets

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Abstract

The classical ID3 decision tree algorithm cannot directly handle continuous data and has a poor classification effect. Moreover, most of the existing approaches use a single mechanism for node measurement, which is unfavorable for the construction of decision trees. In order to solve the above problems, we propose an improved ID3 algorithm (called DIGGI) based on variable precision neighborhood rough sets. First, we introduce the notions of variable precision neighborhood (VPN) equivalence relation and VPN equivalence granule, and probe some basic properties of these notions. Second, we give the model of VPN rough sets and propose two extended measures: the VPN information gain and the VPN Gini index. Finally, we construct a hybrid measure by using the VPN dependence to combine the above two extended measures, and adopt the VPN equivalence granule as the splitting rule of DIGGI. Experimental results show that DIGGI is effective and its accuracy is greatly improved compared with three traditional decision tree algorithms, the neighborhood decision tree (NDT) and variable precision neighborhood decision tree (VPNDT) proposed in the latest literature.

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Funding

This work is supported by National Natural Science Foundation of China under Grant Nos.: 62166001, 61976158, 62266003, Jiangxi Provincial Natural Science Foundation under Grant Nos: 20224BAB212022, and Science and Technology Project of Education Department of Jiangxi Province under Grant Nos: GJJ211435

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Authors and Affiliations

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

    Caihui Liu, Jianying Lai & Bowen Lin

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

    Duoqian Miao

Authors
  1. Caihui Liu
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  2. Jianying Lai
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  3. Bowen Lin
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  4. Duoqian Miao
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Correspondence to Caihui Liu.

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Liu, C., Lai, J., Lin, B. et al. An improved ID3 algorithm based on variable precision neighborhood rough sets. Appl Intell 53, 23641–23654 (2023). https://doi.org/10.1007/s10489-023-04779-y

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