自适应的邻域粗糙集邻域大小取值方法.

The application of neighborhood rough set depends on the value of neighborhood size δ. When using attribute reduction algorithms based on neighborhood rough set, an existing method for determining δ is usually point-type, that is, to specify a value only by human experience. The method does not combine with the actual situation when it is used to determineδ, so the practicability of the algorithms can be further discussed. For this reason, this paper proposed an adaptable method for determining δ, which the biggest characteristic was not determining δ but the interval of δ. It forwardly selected the most appropriate δ in the interval by using a fitness function that was combined with the characteristics of data sets and classifiers. The experimental results show that, compared with the point-type method for determining δ, this method can find reduction sets which number of attributes is less, and classification accuracy is higher. It proves that this method can further improve the practicability of attribute reduction algorithms based on neighborhood rough set. [ABSTRACT FROM AUTHOR]