Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Feature Selection with Selective Sampling
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Learning to Share Meaning in a Multi-Agent System
Autonomous Agents and Multi-Agent Systems
Ontology-guided learning to improve communication between groups of agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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We present a method to improve the positive examples selection by teaching agents in a multi-agent system in which a team of agent peers teach concepts to a learning agent. The basic idea in this method is to let a teacher agent expand the features it uses to describe a concept in its ontology by additional features. This resembles the typical behavior of human teachers who describe concepts from different viewpoints in the hope that one of these viewpoints comes close to the viewpoint of a learner. The extended feature set is then used to select positive examples that together with negative examples are communicated to the learner agent. The learner uses concept learning techniques to integrate the new concept into its own ontology. An experimental evaluation shows a significant learning improvement compared to the previous approach.