Name
Abstract | ||
|---|---|---|
•Propose a new hybrid feature selection algorithm based on BACO and clustering.•Modify linear binary ant system to reduce the search space complexity.•Inject mutation to increase randomness of search space.•Feature clustering to decrease the challenges of processing high-dimensional dataset.•Experiment the method in several real-world social datasets and obtain more efficiency. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.01.016 | Expert Systems with Applications |
Keywords | Field | DocType |
Feature selection,High-dimensional data,Binary ant system,Clustering,Mutation | Simulated annealing,Data mining,Feature vector,Clustering high-dimensional data,Feature selection,Computer science,Curse of dimensionality,Artificial intelligence,Local search (optimization),Cluster analysis,Genetic algorithm,Machine learning | Journal |
Volume | ISSN | Citations |
124 | 0957-4174 | 2 |
PageRank | References | Authors |
0.36 | 80 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaleh Manbari | 1 | 5 | 1.45 |
| Fardin Akhlaghian Tab | 2 | 15 | 4.45 |
| Chiman Salavati | 3 | 5 | 1.78 |