Name
Title | ||
|---|---|---|
Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis. |
Abstract | ||
|---|---|---|
This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TITB.2006.889702 | IEEE Transactions on Information Technology in Biomedicine |
Keywords | Field | DocType |
cancer diagnosis,bagging linear sparse bayesian,model building,learning models,vs method,selected variable,basic vs algorithm,relevance vector machine,bagging technique,real-life medical classification problem,variable selection,squares support vector machine,brain tumor multiclass classification,model prediction,bayesian methods,predictive models,microarray data,support vector machines,cancer,multiclass classification,microarray,magnetic resonance spectroscopy,probability,learning artificial intelligence,least squares support vector machine,svm,bagging | Least squares,Data mining,Bayesian inference,Feature selection,Computer science,Artificial intelligence,Probabilistic logic,Multiclass classification,Kernel (linear algebra),Pattern recognition,Support vector machine,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
11 | 3 | 1089-7771 |
Citations | PageRank | References |
13 | 0.91 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuan Lu | 1 | 13 | 0.91 |
| Andy Devos | 2 | 45 | 4.32 |
| J. A.K. Suykens | 3 | 42 | 3.25 |
| Carles Arús | 4 | 13 | 0.91 |
| S. Van Huffel | 5 | 260 | 32.75 |