Name
Title | ||
|---|---|---|
Combining SVM classifiers using genetic fuzzy systems based on AUC for gene expression data analysis |
Abstract | ||
|---|---|---|
Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve (AUC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUCs. Our experiments show that AUC-based genetic fuzzy SVM fusion model produces not only better AUC but also better accuracy than individual SVM classifiers. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72031-7_45 | ISBRA |
Keywords | Field | DocType |
svm classifier,combining svm,individual svm classifier,roc curve,genetic algorithm,better accuracy,genetic fuzzy system,optimal fuzzy membership function,gene expression data analysis,genetic fuzzy svm fusion,svm classifier fusion model,better auc,gene expression,receiver operator characteristic,support vector machine,machine learning | Receiver operating characteristic,Pattern recognition,Computer science,Support vector machine,Fuzzy logic,Artificial intelligence,Svm classifier,Fuzzy control system,Area under the roc curve,Genetic algorithm,Machine learning,Genetic fuzzy systems | Conference |
Volume | ISSN | Citations |
4463 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiujuan Chen | 1 | 41 | 5.25 |
| Yichuan Zhao | 2 | 84 | 22.37 |
| Yan-Qing Zhang | 3 | 851 | 65.22 |
| Robert Harrison | 4 | 51 | 4.58 |