Name
Title | ||
|---|---|---|
Combined feature selection and cancer prognosis using support vector machine regression. |
Abstract | ||
|---|---|---|
Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L₁-L₂-norm Support Vector Machine (L₁-L₂ SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L₁-L₂ SVM for regression analysis with automatic feature selection. We further improve the L₁-L₂ SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three realworld data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TCBB.2010.119 | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
Keywords | Field | DocType |
censored data,optimisation,l_2 svm,feature selection.,high-dimensional data,good classification performance,medical domain,cancer prognosis,l1-l2-norm support vector machine,optimization problem,cancer,support vector machine,combined feature selection,medium performance,medical computing,feature selection,prognostic prediction,support vector machine regression,support vector machines,effective classification technique,patient treatment,automatic feature selection,normal prognostic method,prognostic prediction method,regression analysis,computational biology,high dimensional data,linear regression | Data set,Feature selection,Pattern recognition,Computer science,Regression analysis,Support vector machine,Svm regression,Artificial intelligence,Censoring (statistics),Optimization problem,Machine learning,Linear regression | Journal |
Volume | Issue | ISSN |
8 | 6 | 1545-5963 |
Citations | PageRank | References |
6 | 0.46 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingyu Sun | 1 | 358 | 23.31 |
| Zhi-Hua Zhu | 2 | 14 | 1.44 |
| Jiuyong Li | 3 | 1206 | 107.58 |
| Bin Linghu | 4 | 6 | 0.80 |