Name
Abstract | ||
|---|---|---|
•A new two-stage feature selection methodology for high-dimensional regression problems is proposed.•It is called wide spectrum feature selection for regression (WiSe).•Pearson correlation, Spearman rank correlation, Symmetrical uncertainty, and all their pairwise combinations are considered in the first stage.•In the second stage, the first-pass screened features are further selected using regression-dependent approaches.•Results confirm the increased predictive accuracy of models built after filtering out irrelevant features. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compchemeng.2018.10.005 | Computers & Chemical Engineering |
Keywords | Field | DocType |
Feature selection,Filtering methods,Predictive analytics,Effect sparsity,Symmetrical uncertainty | Predictive methods,Mathematical optimization,Regression,Feature selection,Regression analysis,Robustness (computer science),Artificial intelligence,Regression problems,Bivariate analysis,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
121 | 0098-1354 | 0 |
PageRank | References | Authors |
0.34 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo R. Rendall | 1 | 2 | 1.47 |
| Ivan Castillo | 2 | 2 | 1.71 |
| Alix Schmidt | 3 | 0 | 0.34 |
| Swee-Teng Chin | 4 | 0 | 0.34 |
| Leo H. Chiang | 5 | 89 | 8.72 |
| Marco S. Reis | 6 | 13 | 6.49 |