Instance-Based Learning Algorithms
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning
Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions
Computational Economics
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adaptive negotiation agents for e-business
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Learning opponents' preferences in multi-object automated negotiation
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Dynamic integration of classifiers for handling concept drift
Information Fusion
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Protecting a Moving Target: Addressing Web Application Concept Drift
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Improving the distributed constraint optimization using social network analysis
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
| Hi-index | 12.05 |
In this paper, we propose a social approach for learning agents. In dynamic environments, smart agents should detect changes and adapt themselves, applying dynamic learning strategies and drift detection algorithms. Recent studies note that an ensemble of learners can be coordinated by simple protocols based on votes or weighted votes; however, they are not capable of determining the number of learners or the ensemble composition properly. Conversely, we show in this paper that Social Network Theory can provide the multi-agent learning community with sophisticated and well-founded reputation models that outperform well-known ensemble-based drift detection techniques, generating accurate and small ensembles of learning agents. Our approach is evaluated considering dynamic bilateral negotiation scenarios and benchmark databases, presenting statistically significant results that are better than those of other ensemble-based techniques.