Abstract: The current data completion methods based on rough sets do not consider the differences between different condition attributes when calculating the similarities between any two objects．To solve this problem,this paper introduced a new notion of weighted similarity,and proposed a rough set data completion algorithm called RDNAWS based on relative decision entropy and weighted similarity．RDNAWS algorithm adopts the concept of relative decision entropy to measure the significance of each condition attribute．Through calculating the significance of each condition attribute and the dependence of the set of decision attributes on it,RDNAWS provides a weight for each condition attribute,which can efficiently distinguish various condition attributes．The experimental results on real data sets demonstrate that our algorithm can obtain better classification performance than the current algorithms．

Key words: Incomplete data,Rough set,Data completion,Relative decision entropy,Weighted similarity