Name
Abstract | ||
|---|---|---|
Ensemble learning has been successfully used in many areas, due to its powerful ability to solve complex problems. In recent years, some researchers have shown that ensemble of some learners instead of all individual learners could get better performances. However, how to select individual learners as diverse as possible is a very important issue. In this paper, a new diversity measure is proposed to achieve a better selection of individual learners. Different from the commonly used diversity measures, it makes full of the data distribution information provided by the cheap and abundant unlabeled data rather than the expensive and scarce labeled data in order to obtain the higher classification accuracy. The selection method based on the new diversity measure is simple in computation and independent of models. Experimental results demonstrate its good performances. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CICSyN.2012.14 | CICSyN |
Keywords | Field | DocType |
data distribution information,selection method,individual learner,better selection,abundant unlabeled data,new diversity measure,ensemble learning,unlabeled data,new diverse measure,diversity measure,better performance,complex problem,computational modeling,machine learning,neural networks,learning artificial intelligence,correlation,data handling | Online machine learning,Data mining,Semi-supervised learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Artificial neural network,Ensemble learning,Group method of data handling,Machine learning,Computation | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Chu | 1 | 5 | 1.80 |
| Min Wang | 2 | 62 | 4.30 |
| Xiaoqin Zeng | 3 | 407 | 32.97 |
| Lixin Han | 4 | 135 | 14.47 |