Name
Abstract | ||
|---|---|---|
Attribute reduction is the process of removing a subset of attributes from the dataset. One of the most famous tools used for solving the attribute reduction problem is rough set theory. The current attribute reduction methods in rough set theory are failed for finding the optimal reduction because of no perfect heuristic can ensure optimality. In this paper, we consider a novel rough set approach to attribute reduction based on heuristic genetic algorithm. The proposed method, called accelerated genetic algorithm attribute reduction (AGAAR). The proposed method uses new suitable crossover and mutation operators that fit the considered problem. Moreover, an acceleration technique is also invoked in order to accelerate the search process for the optimal reduction. The experiment is archived to AGAAR through 13 well-known datasets from UCI machine learning repository. The experiment proves that the algorithm is more effective, it has improved the global search ability to avoid falling into local optimum, and it can get relative minimum attribute reduction. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SNPD.2015.7176207 | 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) |
Keywords | Field | DocType |
Attribute Reduction,Genetic Algorithm,Rough Sets | Heuristic,Crossover,Local optimum,Computer science,Rough set,Artificial intelligence,Acceleration,Machine learning,Dominance-based rough set approach,Genetic algorithm,Mutation operator | Conference |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdel-Rahman Hedar | 1 | 404 | 30.79 |
| Mohamed Adel Omar | 2 | 1 | 0.36 |
| Adel A. Sewisy | 3 | 20 | 4.33 |