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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]