Name
Title | ||
|---|---|---|
Parameter determination of support vector machine and feature selection using simulated annealing approach |
Abstract | ||
|---|---|---|
Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.asoc.2007.10.012 | Appl. Soft Comput. |
Keywords | Field | DocType |
feature selection,support vector machine,svm training process,simulated annealing approach,novel pattern classification method,classification accuracy,support vector machines,classification accuracy rate,parameter determination,svm classification accuracy,simulated annealing,kernel parameter,grid search,machine learning | Structured support vector machine,Data mining,Feature selection,Computer science,Artificial intelligence,Kernel (linear algebra),Simulated annealing,Hyperparameter optimization,Pattern recognition,Support vector machine,Relevance vector machine,Linear classifier,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 4 | Applied Soft Computing Journal |
Citations | PageRank | References |
96 | 2.87 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih-Wei Lin | 1 | 1059 | 46.26 |
| Zne-Jung Lee | 2 | 940 | 43.45 |
| Shih-Chieh Chen | 3 | 459 | 15.17 |
| Tsung-Yuan Tseng | 4 | 133 | 6.42 |