Name
Title | ||
|---|---|---|
Modulated clustering using integrated rough sets and scatter search attribute reduction. |
Abstract | ||
|---|---|---|
Feature selection is an essential problem in pattern classification systems. The entire performance of the classifier is highly affected by the quality of the selected features. In this paper, we address this problem by integrating feature selection with the clustering process. A novel feature-modulated classification algorithm is proposed to improve the classification accuracy. We use a rough sets approach for feature selection based on a scatter search meta-heuristic scheme. The proposed approach sifts a compact subset of characterizing features in multi-class systems according to the clustering performance. To verify the effectiveness of our method, experimental comparisons are carried out on eleven benchmark datasets using two typical classifiers. The results indicate that the proposed method has a remarkable ability to generate effective reduced-size subsets of salient features while yielding significant classification accuracy.
|
Year | Venue | Field |
|---|---|---|
2018 | GECCO (Companion) | Pattern recognition,Feature selection,Computer science,Rough set,Artificial intelligence,Cluster analysis,Classifier (linguistics),Machine learning,Salient |
DocType | ISBN | Citations |
Conference | 978-1-4503-5764-7 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdel-Rahman Hedar | 1 | 404 | 30.79 |
| Abdel-Monem M. Ibrahim | 2 | 1 | 0.70 |
| Alaa E. Abdel-hakim | 3 | 122 | 9.75 |
| Adel A. Sewisy | 4 | 20 | 4.33 |