A three-way decision methodology to multi-attribute decision-making in multi-scale decision information systems

Network rule extraction under the network formal context based on three-way decision

Knowledge discovery combined with network structure is an emerging field of network data analysis and mining. Three-way concept analysis is a method that can fit the human mind in uncertain decisions and analysis. In reality, when three-way concept analysis is placed in the background of a network, not only the three-way rules need to be obtained, but also the network characteristic values of these rules should be obtained, which is of great significance for concept cognition in the network. This paper mainly combines complex network analysis with the formal context of three-way decision. Firstly, the network formal context of three-way decision (NFC3WD) is proposed to unify the two studies mentioned above into one data framework. Then, the network weaken-concepts of three-way decision (NWC3WD) and their corresponding sub-networks are studied. Therefore, we can not only find out the network weaken-concepts but also know the average influence of the sub-network, as well as the influence difference within the sub-network. Furthermore, the concept logic of network and the properties of its operators are put forward, which lays a foundation for designing the algorithm of rule extraction. Subsequently, the bidirectional rule extraction algorithm and reduction algorithm based on confidence degree are also explored. Meanwhile, these algorithms are applied to the diagnosis examples of COVID-19 from which we can not only get diagnostic rules, but also know the importance of the population corresponding to these diagnostic rules in the network through network eigenvalues. Finally, experimental analysis is made to show the superiority of the proposed method.

Comprehensive evaluation of land reclamation schemes in mining areas based on linguistic intuitionistic fuzzy group decision-making

Schemes to protect the geological environment and reclaim land are core requirements for an application for mining rights and complying with mining regulations. Mining enterprises must be supervised to ensure they fulfill their obligations. To guide oversight of such schemes and to provide references for their compilation and review, this study evaluates land reclamation schemes in mining areas using a multi-attribute group decision-making method. First, linguistic intuitionistic fuzzy numbers are used to describe the evaluation information. Considering the authority and preferences of experts, methods for determining expert weights in four cases are established. Max-min deviation then determines the attribute weights, and a method for linguistic intuitionistic fuzzy group decision-making is proposed. Finally, the practicability of this method is verified through a comparative analysis of land reclamation schemes for four mining areas in Sichuan Province, China. The results show the proposed method to give simple and effective evaluation, making it reasonably applicable to the compilation and review of land reclamation schemes.

Generalized fuzzy variable precision rough sets based on bisimulations and the corresponding decision-making

Recently, the classical rough set has been extended in many ways. However, some of them are based on binary relations which only excavate “one step” information to distinguish objects. The “one step” in the binary relation means that the ordered pair of the starting and end points of the step belongs to the relation. Faced with some complex data sets, the “one step” information may be not feasible. Motivated by the notion of bisimulation in computer science, three types of bisimulation-based generalized fuzzy variable precision rough set (BGFVPRS) models are constructed. Different from many existed rough set models which are based on binary relations, the BGFVPRS models can distinguish objects by excavating the “multi-step” information of underlying relations. The related properties and relationships of BGFVPRS models are investigated. The uncertainty measure of BGFVPRS models and the reduction of fuzzy bisimulations are also discussed. Furthermore, learning from the PROMETHEE II method and combining it with our presented BGFVPRS models, a novel multiple-attribute decision-making method is provided. This method can effectively deal with complex problems including attribute data and relational data. The flexibility and effectiveness of our decision-making method are illustrated by comparative analysis and sensitivity analysis in the Zachary karate club network.

Corrigendum to “Comparison of six types of rough approximations based on j-neighborhood space and j-adhesion neighborhood space"

The main aims of this paper are to show that some results presented in [1] are erroneous. To this end, we provide some counterexamples to demonstrate our claim, and give the correct form of the incorrect results in [1]. Also, some improvements for the definition of accuracy measure is proposed. Furthermore, we show that the relationships given in the three figures need not be true in general, and determine the conditions under which they are correct. Finally, a medical application in the decision-making of the diagnosis of dengue fever is examined.