Name
Abstract | ||
|---|---|---|
Balanced ensemble learning is developed from negative correlation learning by shifting the learning targets. Compared to the negative correlation learning, balanced ensemble learning is able to learn faster and achieve the higher accuracy on the training sets for a number of the tested classification problems. However, it has been found that the higher accuracy balanced ensemble learning obtained on the training sets, the higher risks it might be trapped in overfitting. In order to lessen the degree of overfitting in balanced ensemble learning, two parameters of the lower bound of error rate (LBER) and the upper bound of error output (UBEO) were set to decide whether a training point should be learned or ignored in the learning process. Such selective learning could prevent the ensembles from learning too much on the training set to have a good performance on the testing set. This paper show how LBER and UBEO would affect the performance of balanced ensemble learning in view of correlation control. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICNC.2014.6975801 | ICNC |
Keywords | DocType | ISSN |
training point,negative correlation learning,learning (artificial intelligence),LBER,pattern classification,correlation control,training sets,balanced ensemble learning,tested classification problems,lower bound of error rate,upper bound of error output,UBEO | Conference | 2469-8814 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Liu | 1 | 2526 | 265.08 |
| Qiangfu Zhao | 2 | 214 | 62.36 |
| Yan Pei | 3 | 125 | 22.89 |