Name
Abstract | ||
|---|---|---|
Feature selection is a task of great importance. Many feature selection methods have been proposed, and can be divided generally
into two groups based on their dependence on the learning algorithm/classifier. Recently, a feature selection method that
selects features at the same time as it evolves neural networks that use those features as inputs called Feature Selective
NeuroEvolution of Augmenting Topologies (FS-NEAT) was proposed by Whiteson et al. In this paper, a novel feature selection
method called Feature Deselective NeuroEvolution of Augmenting Topologies (FD-NEAT) is presented. FD-NEAT begins with fully
connected inputs in its networks, and drops irrelevant or redundant inputs as evolution progresses. Herein, the performances
of FD-NEAT, FS-NEAT and traditional NEAT are compared in some mathematical problems, and in a challenging race car simulator
domain (RARS). On the whole, the results show that FD-NEAT significantly outperforms FS-NEAT in terms of network performance
and feature selection, and evolves networks that offer the best compromise between network size and performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s12065-009-0018-z | Evolutionary Intelligence |
Keywords | Field | DocType |
neural networksgenetic algorithms � evolutionlearning,network performance,feature selection | Feature selection,Pattern recognition,Computer science,Neuroevolution of augmenting topologies,Artificial intelligence,Artificial neural network,Neuroevolution,Classifier (linguistics),Machine learning,Genetic algorithm,Network performance,Mathematical problem | Journal |
Volume | Issue | ISSN |
1 | 4 | 1864-5917 |
Citations | PageRank | References |
10 | 0.58 | 26 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maxine Tan | 1 | 44 | 10.61 |
| Michael I. Hartley | 2 | 47 | 8.06 |
| Michel Bister | 3 | 32 | 4.75 |
| Rudi Deklerck | 4 | 131 | 12.63 |