| Title | Efficient attribute reduction based on rough sets and differential evolution algorithm |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Jing, Si-Yuan, Yang, Jun |
| Conference Name | 2020 16th International Conference on Computational Intelligence and Security (CIS) |
| Keywords | attribute reduction, Classification algorithms, composability, compositionality, Computational Intelligence, convergence, cryptography, differential evolution, filter method, Heuristic algorithms, hybrid intelligence, pubcrawl, rough set theory, Rough sets, security |
| Abstract | Attribute reduction algorithms in rough set theory can be classified into two groups, i.e. heuristics algorithms and computational intelligence algorithms. The former has good search efficiency but it can not find the global optimal reduction. Conversely, the latter is possible to find global optimal reduction but usually suffers from premature convergence. To address this problem, this paper proposes a two-stage algorithm for finding high quality reduction. In first stage, a classical differential evolution algorithm is employed to rapidly approach the optimal solution. When the premature convergence is detected, a local search algorithm which is intuitively a forward-backward heuristics is launched to improve the quality of the reduction. Experiments were performed on six UCI data sets and the results show that the proposed algorithm can outperform the existing computational intelligence algorithms. |
| DOI | 10.1109/CIS52066.2020.00054 |
| Citation Key | jing_efficient_2020 |
Efficient attribute reduction based on rough sets and differential evolution algorithm