A methodology for obtaining the discrimable matrix or table (more decisive attributes) and knowledge reduction for the above information system is proposed.
In this paper, a hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm (HCMRKR) with robust ensemble Pareto dominance is proposed for big data analysis.
Third, we develop several measurements by using the belief and plausibility functions, and characterize knowledge reduction of multigranulation spaces based on these measurements.
Finally, we explore several examples to illustrate that the proposed approaches are feasible to perform knowledge reduction of dynamic covering information systems.
Finally, we employ several examples to illustrate the feasibility of the incremental approaches for knowledge reduction of dynamic covering decision information systems when increasing the cardinalities of coverings.
In this paper, we aim to construct belief structures and characterize knowledge reduction in terms of evidence theory for the multigranulation spaces where decision attributes are considered.
Thirdly, various elitist leaders will use the parallel MapReduce mechanism to determine the best strategy to extract their knowledge reduction set, and the adaptive reduction evolution with incremental learning can be implemented well.
Experimental results illustrate that the proposed HCMRKR algorithm can effectively and efficiently handle the knowledge reduction problem in the big data sets as well as keeping more robust and stable performances, compared with existing state-of-the-art algorithms.
In covering-based rough set theory, non-incremental approaches are time-consuming for performing knowledge reduction of dynamic covering decision information systems when the cardinalities of coverings change as a result of object immigration and emigration.
Because computing approximations of sets is an important step for knowledge reduction of dynamic covering decision information systems, efficient approaches to calculating the second and sixth lower and upper approximations of sets using the type-1 and type-2 characteristic matrices, respectively, are essential.
