Fast approach to knowledge acquisition in covering information systems using matrix operations

Abstract

Covering rough set theory provides an effective approach to dealing with uncertainty in data analysis. Knowledge acquisition is a main issue in covering rough set theory. However, the original rough set methods are still expensive for this issue in terms of time consumption. To further improvement, we propose fast approaches to knowledge acquisition in covering information systems by employing novel matrix operations. Firstly, several matrix operations are introduced to compute set approximations and reducts of a covering information system. Then, based on the proposed matrix operations, the knowledge acquisition algorithms are designed. In the end, experiments are conducted to illustrate that the new algorithms can dramatically reduce the time consumptions for computing set approximations and reducts of a covering information system, and the larger the scale of a data set is, the better the new algorithms perform.