Random sampling accelerator for attribute reduction

Abstract As one of the crucial topics in the development of rough set, attribute reduction has received extensive attentions because it is practical and interpretable for us to perform dimensional reduction or feature selection. Currently, to further improve the efficiency of searching reducts, many researchers have devoted themselves to designing various accelerative mechanisms. Among these existing results, it should be pointed out that the accelerators designed by reducing the scale of samples strongly depend on the distribution of data. To fill such a gap, an accelerator based on the random sampling is developed. The superiorities of our accelerator are: (1) randomly selecting samples without considering the distribution of data; (2) guidance-based evaluations and selections of attributes; (3) easily to be combined with other popular searching strategies. By comparing with 5 state-of-the-art accelerators over 24 UCI data sets: (1) our accelerator may significantly reduce the time consumption of deriving reducts and then the average speed-up ratio can exceed 10; (2) the reduct derived by our accelerator can offer competent performance in classification task.