科研之友
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摘要
As an extension of the soft set, the bijective soft set can be used to mine data from soft set environments, and has been studied and applied in some fields. However, only a small proportion of fault data will cause bijective soft sets losing major recognition ability for mining data. Therefore, this study aims to improve the bijective soft set-based data mining method on tolerate-fault-data ability. First some notions and operations of the bijective soft set at a p-misclassification degree is defined. Moreover, algorithms for finding an optimal p, reductions, cores, decision rules and misclassified data are proposed. This paper uses a real problem in gaining shoreline resources evaluation rules to validate the model. The results show that the proposed model has the fault-tolerant ability, and it improves the tolerate-ability of bijective soft set-based data mining method. Moreover, the proposed method can help decision makers to discover fault data for further analysis.
