Name
Abstract | ||
|---|---|---|
•Supervised feature selection (FS) approach for high dimensional datasets with small sample size.•Formulated FS as a set-covering problem by defining instance votes to features.•Achieved lower misclassification rates on average as compared to MI based methods.•Selected features were robust to minor data variations.•Stopping criterion automatically determines size of selected feature subsets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.07.018 | Information Sciences |
Keywords | Field | DocType |
Feature selection,Filter-based method,Set-covering problem,Instance voting,Graph modularity,Priority coverage | Decision rule,Heuristic,Voting,Feature selection,Artificial intelligence,Mutual information,Small set,Machine learning,Maximization,Mathematics,Bayes' theorem | Journal |
Volume | ISSN | Citations |
504 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lily Chamakura | 1 | 0 | 0.68 |
| Goutam Saha | 2 | 255 | 23.17 |