Name
Abstract | ||
|---|---|---|
This study extends the recently proposed Evolving Spiking Neural Network (ESNN) architecture by combining it with an optimization algorithm, namely the Versatile Quantum-inspired Evolutionary Algorithm (vQEA). Following the wrapper approach, the method is used to identify relevant feature subsets and simultaneously evolve an optimal ESNN parameter setting. Applied to carefully designed benchmark data, containing irrelevant and redundant features of varying information quality, the ESNN-based feature selection procedure lead to excellent classification results and an accurate detection of relevant information in the dataset. Redundant and irrelevant features were rejected successively and in the order of the degree of information they contained. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-642-02490-0_149 | ICONIP (1) |
Keywords | Field | DocType |
redundant feature,optimal esnn parameter setting,spiking neural network,relevant feature subsets,irrelevant feature,parameter optimization,varying information quality,versatile quantum-inspired evolutionary algorithm,relevant information,integrated feature,accurate detection,esnn-based feature selection procedure,information quality,feature selection | Data mining,Pattern recognition,Evolutionary algorithm,Feature selection,Computer science,Artificial intelligence,Optimization algorithm,Spiking neural network,Machine learning,Information quality | Conference |
Volume | ISSN | ISBN |
5506 | 0302-9743 | 3-642-02489-0 |
Citations | PageRank | References |
21 | 0.97 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Schliebs | 1 | 380 | 18.56 |
| Michaël Defoin-Platel | 2 | 46 | 2.24 |
| Nikola K Kasabov | 3 | 3645 | 290.73 |