A Bisection Method for Information System Knowledge Reduction

In rough set theory, attribute reduction aims to retain the discernability of the original attribute set, and many attribute reduction algorithms have been proposed in literatures. However, these methods are computationally time-consuming for large scale datasets. We develop a bisection method for attribute reduction and the main opinion is to partition the universe into smaller ones by using partition core attributes to reduce the complexity. Experiments and analysis show that, compared with the traditional unbisection reduction algorithm, the developed bisection algorithm can significantly reduce computational time while maintaining their results as same as before.