C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
A comparison study of strategies for combining classifiers from distributed data sources
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A-Teams and Their Applications
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
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Distributed data mining (DDM) is an important research area. One of the approaches suitable for the DDM is to select relevant local patterns from the distributed databases. Such patterns, often called prototypes, are subsequently merged to create a compact representation of the distributed data repositories. In the paper the local prototype selection is carried out independently at each site where instances and features are selected simultaneously by teams of agents. To assure obtaining homogenous prototypes the feature selection requires collaboration of agents. In the paper two agent collaboration strategies producing a common set of features are proposed and experimentally validated. The paper includes a detailed description of the proposed approach and a discussion of the computational experiment results.