Machine Learning
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Machine Learning
SMILE: Sound Multi-agent Incremental LEarning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A framework for agent-based distributed machine learning and data mining
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A Framework for Knowledge Discovery in a Society of Agents
DS '08 Proceedings of the 11th International Conference on Discovery Science
Multiagent Incremental Learning in Networks
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
MALEF: Framework for distributed machine learning and data mining
International Journal of Intelligent Information and Database Systems
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Learning agents can improve performance cooperating with other agents, particularly learning agents forming a committee outperform individual agents. This "ensemble effect" is well known for multi-classifier systems in Machine Learning. However, multi-classifier systems assume all data is known to all classifiers while we focus on agents that learn from cases (examples) that are owned and stored individually. In this article we focus on how individual agents can engage in bargaining activities that improve the performance of both individual agents and the committee. The agents are capable of self-evaluation and determining that some data used for learning is unnecessary. This "refuse" data can then be exploited by other agents that might found some part of it profitable to improve their performance. The experiments we performed show that this approach improves both individual and committee performance and we analyze how these results in terms of the "ensemble effect".