D. Song, W. Ru-Chuan, F. Xiong, and Y. Le-Chan
Gene expression programming, attribution reduction, expressiontree, intelligence computing
This paper presents gene expression programming for attribution reduction in rough set (GEP-ARRS), which designs a new GEP code to convert attribution reduction into an expression tree and a new fitness function. Meanwhile, to solve optimal reduction, GEP- ARRS implements a dynamic population creation strategy to reduce the gene length of GEP to accelerate solution efficiency of GEP. Through extensive experiments on mass or high-dimensional data sets, it is shown that GEP-ARRS is apparently more advantageous in terms of speed and quality in contrast with traditional attribution reduction algorithms on intelligence computing.
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