Abstract:
This is era of information and on daily basis we have to deal with the huge amount of data.
Various tools and techniques are available for processing such huge volumes of data and Rough
Set Theory (RST) is one of the most prominent tools for this purpose. However, it does not
consider the preference order between the values of the attributes. Dominance Based Rough Set
Approach (DRSA) is the one that provides dominance relation in this regard. In DRSA the lower
and upper approximations are two measures that form basis of the most of the algorithms based
on DRSA. However, computing these approximations is computationally so expensive that the
algorithms using these measures may suffer serious performance bottlenecks while dealing with
datasets beyond smaller size. In this paper, we have proposed a new approach to compute these
measures. The proposed algorithm directly calculates approximations without considering the
objects that do not play any role in approximations. The proposed approach was compared with
the conventional method using ten benchmark datasets from UCI. Results have shown that the
proposed approach significantly reduces the execution time. Average reduction in execution time
was found to be 80%. The proposed approach also reduces the complexity (Big-O). This approach
also reduces memory consumption up to 75%.