A parallel rule-based approach to compute rough approximations of dominance based rough set theory. (October 2022) - Nosheen, Faryal; Qamar, Usman; Raza, Muhammad Summair
Abstract: In many datasets, conditional attributes and decision classes are preference-ordered, however, the classical Rough Set Theory (RST) does not consider the preference order between the values of the attributes. An extension of RST known as a Dominance-based Rough Set Approach (DRSA) provides dominance relation in this regard. In DRSA, data analysis mainly depends on the calculations of lower and upper approximations and these two measures are computationally utilizing many resources i.e., time and memory, due to the consideration of preference order. In this paper, we have proposed a parallel technique for calculating DRSA approximation sets. The proposed method directly computes approximations by following heuristic rules without calculating dominance positive or negative relations. The proposed parallel approach is then compared with the conventional method of calculation of DRSA approximations and a recent another technique of parallel processing using ten UCI publicly available datasets. Results validated the efficiency and effectiveness of the proposed model. An average reduction of 83% was observed in execution time and 86% in memory consumption. The structural complexity of the algorithm also considerably reduced. Highlights: Dominance-based Rough Set Approach (DSRA) prominent tools for feature selection. A new approach to compute these measures is proposed. The average reduction in execution time was found to be 83%. The structural complexity of the algorithm also considerably reduced. Average memory consumption is also reduced by 86%.