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<p>Interval sets, proposed by Yao, consist of the upper and lower-bound sets which are finite sets. However, in practical situations, the upper and lower-bound sets may be infinite sets. In this paper, we first propose the concept of generalized interval sets and define the distance between generalized interval sets. Then, we introduce a generalized interval set information system (GIS), in which the conditional attribute values are generalized interval sets and the decision attribute values are set-valued or single-valued. We mine the effect of different conditional attributes on decision making in the GIS in order to assign weight to each conditional attribute. Subsequently, we give the definition of generalized weighted neighborhood rough sets (GWNRS) in the GIS. Dependency degree is proposed based on GWNRS to evaluate the significance of attribute subsets. Furthermore, we use greedy search algorithm to perform attribute reduction in the GIS and evaluate the classification performance with SVM and KNN classifiers. In this process, we find the optimal neighborhood threshold by isometric search. Finally, we conduct experiments on six UCI datasets to validate the performance of the attribute reduction algorithm proposed in this paper.</p>

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Attribute reduction based on generalized weighted neighborhood rough sets in generalized interval set information systems

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Hai-Long Yang,
  • He Wang,
  • Zhi-Lian Guo

摘要

Interval sets, proposed by Yao, consist of the upper and lower-bound sets which are finite sets. However, in practical situations, the upper and lower-bound sets may be infinite sets. In this paper, we first propose the concept of generalized interval sets and define the distance between generalized interval sets. Then, we introduce a generalized interval set information system (GIS), in which the conditional attribute values are generalized interval sets and the decision attribute values are set-valued or single-valued. We mine the effect of different conditional attributes on decision making in the GIS in order to assign weight to each conditional attribute. Subsequently, we give the definition of generalized weighted neighborhood rough sets (GWNRS) in the GIS. Dependency degree is proposed based on GWNRS to evaluate the significance of attribute subsets. Furthermore, we use greedy search algorithm to perform attribute reduction in the GIS and evaluate the classification performance with SVM and KNN classifiers. In this process, we find the optimal neighborhood threshold by isometric search. Finally, we conduct experiments on six UCI datasets to validate the performance of the attribute reduction algorithm proposed in this paper.