Name
Abstract | ||
|---|---|---|
In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2015.2504957 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Training,Optimization,Training data,Neural networks,Linear programming,Minimization,Learning systems | Ranking SVM,Feature selection,Computer science,Artificial intelligence,Linear programming,Artificial neural network,Feedforward neural network,Mathematical optimization,Global optimization,Pattern recognition,CPU time,Curse of dimensionality,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 4 | 2162-237X |
Citations | PageRank | References |
4 | 0.41 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Bravi | 1 | 5 | 0.76 |
| Veronica Piccialli | 2 | 259 | 20.63 |
| M. Sciandrone | 3 | 335 | 29.01 |