Name
Title | ||
|---|---|---|
A method for feature selection based on the optimal hyperplane of SVM and independent analysis |
Abstract | ||
|---|---|---|
Feature selection is an important topic in machine learning. In order to evaluate the candidate features, a strategy based on the constituent principle of the SVM optimal hyperplane is established in this paper. Then, by considering different feature combinations, a better feature subset can be obtained. The method is used to recognize the monomers in weather forecast, and experimental results demonstrate its effectiveness in enhancing the classification performance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICMLC.2013.6890454 | ICMLC |
Keywords | Field | DocType |
svm optimal hyperplane,the optimal hyperplane,feature subset,learning (artificial intelligence),feature selection,support vector machine,molecules,correlation analysis,weather forecast,machine learning,independent analysis,weather forecasting,support vector machines,monomers,learning artificial intelligence | Pattern recognition,Feature selection,Computer science,Support vector machine,Artificial intelligence,Hyperplane,Weather forecasting,Correlation analysis,Machine learning | Conference |
Volume | ISSN | Citations |
01 | 2160-133X | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin-Fang Hu | 1 | 0 | 0.34 |
| Wei Gong | 2 | 0 | 0.34 |
| Li-Xiao Qi | 3 | 0 | 0.34 |
| Ping Wang | 4 | 0 | 0.34 |