Name
Abstract | ||
|---|---|---|
We propose a new approach to feature selection that is based on ensembling and meta-learning. Meta-learning is used to choose feature selection algorithms from a preselected that are used to produce feature rankings, which are then aggregated into a resulting feature ranking. This approach requires a lot of additional computational time for meta-learning system construction, but it works fast and shows better feature selection quality than algorithms in aggregation. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI) | machine learning, feature selection, meta-learning, algorithms ensembling |
Field | DocType | Citations |
Markov process,Feature selection,Pattern recognition,Computer science,Feature ranking,Algorithm,Feature extraction,Prediction algorithms,System construction,Artificial intelligence,Signal processing algorithms | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Tanfilev | 1 | 0 | 0.34 |
| Andrey Filchenkov | 2 | 46 | 15.80 |
| Ivan Smetannikov | 3 | 1 | 1.37 |