Name
Abstract | ||
|---|---|---|
Gene selection methods available have high computational complexity. This paper applies an 1-norm support vector machine with the squared loss (1-norm SVMSL) to implement fast gene selection for cancer classification. The 1-norm SVMSL, a variant of the 1-norm support vector machine (1-norm SVM) has been proposed. Basically, the 1-norm SVMSL can perform gene selection and classification at the same. However, to improve classification performance, we only use the 1-norm SVMSL as a gene selector, and adopt a subsequent classifier to classify the selected genes. We perform extensive experiments on four DNA microarray data sets. Experimental results indicate that the 1-norm SVMSL has a very fast gene selection speed compared with other methods. For example, the 1-norm SVMSL is almost an order of magnitude faster than the 1-norm SVM, and at least four orders of magnitude faster than SVM-RFE (recursive feature elimination), a state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10489-017-1056-3 | Appl. Intell. |
Keywords | Field | DocType |
Support vector machine,Gene selection,Cancer classification,1-norm support vector machine,Orthogonal matching pursuit | Matching pursuit,Structured support vector machine,Square (algebra),Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Classifier (linguistics),Recursion,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
48 | 7 | 0924-669X |
Citations | PageRank | References |
2 | 0.39 | 31 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 363 | 39.03 |
| Wei-Da Zhou | 2 | 256 | 16.01 |
| Bangjun Wang | 3 | 28 | 4.89 |
| Zhao Zhang | 4 | 938 | 65.99 |
| fanzhang li | 5 | 75 | 8.73 |