A Bayes fusion method based ensemble classification approach for Brown cloud application
| dc.category | Journal Article | |
| dc.contributor.author | Subashini, P | |
| dc.date.accessioned | 2017-03-29T22:01:08Z | |
| dc.date.available | 2017-03-29T22:01:08Z | |
| dc.date.issued | 2014 | |
| dc.department | Computer Science | en_US |
| dc.description.abstract | Classification is a recurrent task o f determining a target function that maps each attribute set to one o f the predefined class labels. Ensemble fusion is one o f the suitable classifier model fusion techniques which combine the multiple classifiers to perform high classification accuracy than individual classifiers. The main objective o f this paper is to combine base classifiers using ensemble fusion methods namely Decision Template, Dempster- Shafer and Bayes to compare the accuracy o f the each fusion methods on the brown cloud dataset. The base classifiers like KNN, MLP and SVM have been considered in ensemble classification in which each classifier with fo u r different function parameters. From the experimental study it is proved, that the Bayes fusion method performs better classification accuracy o f 95% than Decision Template o f 80%, Dempster-Shaferof 85%, in a Brown Cloud image dataset. | en_US |
| dc.identifier.uri | https://ir.avinuty.ac.in/handle/avu/2302 | |
| dc.lang | English | en_US |
| dc.publisher.name | International Journal of Advanced Computer Research | en_US |
| dc.publisher.type | International | en_US |
| dc.title | A Bayes fusion method based ensemble classification approach for Brown cloud application | en_US |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- CS.international.2014_021.pdf
- Size:
- 3.21 MB
- Format:
- Adobe Portable Document Format