Name
Title | ||
|---|---|---|
A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes. |
Abstract | ||
|---|---|---|
•A new feature selection approach based on Geometric Semantic Genetic Programming (GSGP).•Promising and accurate results.•More contributions to GSGP-related literature.•An extensive experimental evaluation is conducted.•New insights about future research concerning GSGP. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.patrec.2017.10.002 | Pattern Recognition Letters |
Keywords | Field | DocType |
Feature selection,Geometric Semantic Genetic Programming,Optimum-path forest | Feature vector,Fitness landscape,Pattern recognition,Feature selection,Feature (computer vision),Fitness function,Genetic programming,Artificial intelligence,Overfitting,Mathematics,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
100 | C | 0167-8655 |
Citations | PageRank | References |
2 | 0.36 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Paulo Papa | 1 | 278 | 44.60 |
| Gustavo H. Rosa | 2 | 47 | 8.00 |
| luciene patrici papa | 3 | 10 | 2.61 |