Name
Abstract | ||
|---|---|---|
This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results. At the same time, dynamic weighted is introduced into ant colony algorithm for feature selection in order to keep a good balance between the convergence rate and the stagnant phenomenon. The experimental comparison verifies that the algorithm has good classification accuracy and time efficiency. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLC.2010.5581009 | ICMLC |
Keywords | Field | DocType |
optimisation,feature selection,support vector machine (svm),pattern classification,graph model,ant colony algorithm,support vector machine classifier,svm,support vector machine,dynamic weighted ant colony algorithm,graph theory,mutual information(mi),support vector machines,graph nodes,classification algorithms,accuracy,algorithm design and analysis,convergence rate,mutual information,machine learning | Ant colony optimization algorithms,Feature selection,Computer science,Artificial intelligence,Rate of convergence,Graph model,Graph theory,Algorithm design,Pattern recognition,Support vector machine,Algorithm,Statistical classification,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-1-4244-6526-2 | 2 |
PageRank | References | Authors |
0.37 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shang-Hua Xiong | 1 | 2 | 0.37 |
| Ji-yi Wang | 2 | 17 | 8.05 |
| Huang Lin | 3 | 2 | 1.05 |