Variable Precision Rough Set Approximations in Concept Lattice

The notions of variable precision rough set and concept lattice are can be shared by a basic notion, which is the definability of a set of objects based on a set of properties. The two theories of rough set and concept lattice can be compared, combined and applied to each other based on definability. Based on introducing the definitions of variable precision rough set and concept lattice, this paper shows that any extension of a concept in concept lattice is an equivalence class of variable precision rough set. After that, we present a definition of lower and upper approximations in concept lattice and generate the lower and upper approximations concept of concept lattice. Afterwards, we discuss the properties of the new lower and upper approximations. Finally, an example is given to show the validity of the properties that the lower and upper approximations have

Characterizing reducts in multi-adjoint concept lattices

The construction of reducts, that is, minimal sets of attributes containing the main in- formation of a database, is a fundamental task in different frameworks, such as in For- mal Concept Analysis (FCA) and Rough Set Theory (RST). This paper will be focused on a general fuzzy extension of FCA, called multi-adjoint concept lattice, and we present a study about the attributes generating meet-irreducible elements and on the reducts in this framework. From this study, we introduce interesting results on the cardinality of reducts and the consequences in the classical case.Partially supported by the Spanish Economy and Competitiveness Ministry (MINECO) project TIN2016-76653-

Attribute Classification and Reduct Computation in Multi-Adjoint Concept Lattices

The problem of reducing information in databases is an important topic in formal concept analysis, which has been studied in several articles. In this article, we consider the fuzzy en- vironment of the multi-adjoint concept lattices, since it is a general fuzzy framework that allows us to easily establish degrees of pref- erence on the elements of the considered database. We introduce algorithms to discover the information contained in the relational system. By means of these algorithms, we classify the attributes of a multi-adjoint context, and build a minimal subset of attributes preserving the information of the original knowledge system.The work of L. Antoni was supported in part by the Slovak Research and Development Agency under Contract APVV-15-0091. The work of M. E. Cornejo, J. Medina, and E. Ramírez-Poussa was supported in part by the Spanish Economy and Competitiveness Ministry (MINECO) under Project TIN2016-76653-P, in part by the Department of Economy, Knowl- edge, Business and University of the Regional Government of Andalusia in project FEDER-UCA18-108612, and in part by the European Cooperation in Science & Technology (COST) Action CA17124

Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic Decision

Multigranulation computing, which adequately embodies the model of human intelligence in process of solving complex problems, is aimed at decomposing the complex problem into many subproblems in different granularity spaces, and then the subproblems will be solved and synthesized for obtaining the solution of original problem. In this paper, an efficient binary classification of multigranulation searching algorithm which has optimal-mathematical expectation of classification times for classifying the objects of the whole domain is established. And it can solve the binary classification problems based on both multigranulation computing mechanism and probability statistic principle, such as the blood analysis case. Given the binary classifier, the negative sample ratio, and the total number of objects in domain, this model can search the minimum mathematical expectation of classification times and the optimal classification granularity spaces for mining all the negative samples. And the experimental results demonstrate that, with the granules divided into many subgranules, the efficiency of the proposed method gradually increases and tends to be stable. In addition, the complexity for solving problem is extremely reduced

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches