دانلود رایگان مقاله انگلیسی کاهش جنبه های مرتبط با خانواده ها در سیستم های اطلاعات تصمیم گیری پوششی پویا - الزویر 2018

Abstract

Many efforts have focused on studying techniques for selecting most informative features from data sets. Especially, the related family-based approaches have been provided for attribute reduction of covering information systems. However, the existing related family-based methods have to recompute reducts for dynamic covering decision information systems. In this paper, firstly, we investigate the mechanisms of updating the related families and attribute reducts by the utilization of previously learned results in dynamic covering decision information systems with variations of attributes. Then, we design incremental algorithms for attribute reduction of dynamic covering decision information systems in terms of attribute arriving and leaving using the related families and employ examples to demonstrate that how to update attribute reducts with the proposed algorithms. Finally, experimental comparisons with the non-incremental algorithms on UCI data sets illustrate that the proposed incremental algorithms are feasible and efficient to conduct attribute reduction of dynamic covering decision information systems with immigration and emigration of attributes.

6 Conclusions

Knowledge reduction of dynamic covering information systems is a significant challenge of coveringbased rough sets. In this paper, firstly, we have analyzed the related families-based mechanisms of constructing attribute reducts of dynamic covering decision information systems with variations of attributes and employed examples to illustrate how to compute attribute reducts of dynamic covering decision information systems when varying attribute sets. Secondly, we have presented the related families-based heuristic algorithms for computing attribute reducts of dynamic covering decision information systems with attribute arriving and leaving and employed examples to demonstrate how to update attribute reducts with the heuristic algorithms. Finally, we have employed the experimental results to illustrate that the related families-based incremental approaches are effective and feasible for attribute reduction of dynamic covering decision information systems.

In the future, we will study knowledge reduction of dynamic covering decision information systems with variations of object sets. Especially, we will provide effective algorithms for knowledge reduction of dynamic covering decision information systems when object sets are varying with time.