.png){kind=icon}
.png){kind=icon}
Return
Ensemble Pruning Based on Objection Maximization With a General Distributed Framework
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2019.2945116.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and diversity serve as two crucial factors, while they usually conflict with each other. To balance both of them, we formalize the ensemble pruning problem as an objection maximization problem based on information entropy. Then we propose an ensemble pruning method, including a centralized version and a distributed version, in which the latter is to speed up the former. Finally, we extract a general distributed framework for ensemble pruning, which can be widely suitable for most of the existing ensemble pruning methods and achieve less time-consuming without much accuracy degradation. Experimental results validate the efficiency of our framework and methods, particularly concerning a remarkable improvement of the execution speed, accompanied by gratifying accuracy performance.
Keywords:
Information entropy
Diversity reception
Optimization
Degradation
Entropy
Measurement uncertainty
Learning systems
Composable core-sets
diversity
ensemble learning
ensemble pruning
information entropy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
7.5K
Citations:
7.2W