Name
Abstract | ||
|---|---|---|
In this paper, an optimized hierarchical B-spline network was employed to detect the breast cancel. For evolving a hierarchical B-spline network model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and Wavelet Neural Network (WNN) by using the same breast cancer data set. Simulation results show that the obtained hierarchical B-spline network model has a fewer number of variables with reduced number of input features and with the high detection accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11881070_4 | ICNC (1) |
Keywords | Field | DocType |
optimal detection model,optimized hierarchical b-spline network,reduced number,high detection accuracy,neural network,breast cancer data,fewer number,wavelet neural network,flexible neural tree,hierarchical b-spline network,hierarchical b-spline network model,breast cancer detection,breast cancer,network model,evolutionary algorithm,tree structure | Particle swarm optimization,B-spline,Hierarchical control system,Evolutionary algorithm,Computer science,Swarm intelligence,Algorithm,Tree structure,Artificial intelligence,Artificial neural network,Network model,Machine learning | Conference |
Volume | ISSN | ISBN |
4221 | 0302-9743 | 3-540-45901-4 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuehui Chen | 1 | 1167 | 106.13 |
| Mingjun Liu | 2 | 0 | 2.70 |
| Bo Yang | 3 | 519 | 52.33 |