Fault-tolerant enhanced bijective soft set with applications

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. (C) 2016 Elsevier B.V. All rights reserved.