Name
Abstract | ||
|---|---|---|
Ensemble learning is one of the main directions in machine learning and data mining, which allows learners to achieve higher training accuracy and better generalization ability. In this paper, with an aim at improving generalization performance, a novel approach to construct an ensemble of neural networks is proposed. The main contributions of the approach are its diversity measure for selecting diverse individual neural networks and weighted fusion technique for assigning proper weights to the selected individuals. Experimental results demonstrate that the proposed approach is effective. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88906-9_21 | IDEAL |
Keywords | Field | DocType |
data mining,generalization ability,neural network,improving generalization ability,generalization performance,main direction,novel approach,main contribution,neural networks,novel ensemble approach,ensemble learning,diverse individual neural network,machine learning | Diversity measure,Proper weights,Pattern recognition,Computer science,Generalization error,Artificial intelligence,Cluster analysis,Artificial neural network,Ensemble learning,Machine learning | Conference |
Volume | ISSN | Citations |
5326 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Lu | 1 | 164 | 21.93 |
| Xiaoqin Zeng | 2 | 407 | 32.97 |
| Shengli Wu | 3 | 370 | 33.55 |
| Shuiming Zhong | 4 | 79 | 7.30 |