An A-team approach to learning classifiers from distributed data sources

Authors:
Ireneusz Czarnowski;Piotr Jçdrzejowicz;Izabela Wierzbowska
Affiliations:
Department of Information Systems, Gdynia Maritime University, Gdynia, Poland;Department of Information Systems, Gdynia Maritime University, Gdynia, Poland;Department of Information Systems, Gdynia Maritime University, Gdynia, Poland
Venue:
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Year:
2008

Citing 5
Cited 1

A component-based architecture for problem solving environments

Mathematics and Computers in Simulation - IMACS sponsored special issue: 1999 international symposium on computational sciences, to honor John R. Rice
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
Clustering classifiers for knowledge discovery from physically distributed databases

Data & Knowledge Engineering
A Framework for Learning from Distributed Data Using Sufficient Statistics and Its Application to Learning Decision Trees

International Journal of Hybrid Intelligent Systems
JADE-Based a-team environment

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III

A-Teams and Their Applications

ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Distributed data mining is an important research area. The task of the distributed data mining is to analyze data from different sources. Solving such tasks requires a special approach and tools, different from those dedicated to learning from data located in a single database. This paper presents an approach to learning classifiers from distributed data based on data reduction (the prototype selection) at a local level. The problem is solved through applying the A-Team concept implemented using the JABAT environment, which supports implementation of multiple-agent teams. The paper includes a general overview of the JABAT, the problem formulation and some technical details of the proposed implementation. Finally, the computational experiment results validating the approach are shown.