Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Measuring article quality in wikipedia: models and evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Multi-view regression via canonical correlation analysis
COLT'07 Proceedings of the 20th annual conference on Learning theory
Probabilistic quality assessment based on article's revision history
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Automatic Assessment of Document Quality in Web Collaborative Digital Libraries
Journal of Data and Information Quality (JDIQ)
Extracting trust from domain analysis: a case study on the wikipedia project
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
| Hi-index | 0.00 |
The Internet has seen a surge of new types of repositories with free access and collaborative open edition. However, this large amount of information, made available democratically and virtually without any control, raises questions about its quality. In this work, we investigate the use of meta-learning techniques to combine sets of semantically related quality indicators (aka, views) in order to automatically assess the quality of wiki articles. The idea is inspired on the combination of multiple (quality) experts. We perform a thorough analysis of the proposed multiview-based meta-learning approach in 3 collections. In our experiments, meta-learning was able to improve the performance of a state-of-the-art method in all tested datasets, with gains of up to 27% in quality assessment.