Multiagent-Based Model Integration

Authors:
Ana Carolina M. Pilatti de Paula;Braulio C. Avila;Edson Scalabrin;Fabricio Enembreck
Affiliations:
Pontifical Catholic University of Parana, Brazil;Pontifical Catholic University of Parana, Brazil;Pontifical Catholic University of Parana, Brazil;Pontifical Catholic University of Parana, Brazil
Venue:
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Year:
2006

Citing 6
Cited 1

ARCHON: an architecture for multi-agent systems

ARCHON: an architecture for multi-agent systems
Bagging predictors

Machine Learning
Data mining: concepts and techniques

Data mining: concepts and techniques
Principles of data mining

Principles of data mining
Mining Very Large Databases with Parallel Processing

Mining Very Large Databases with Parallel Processing
A Roadmap of Agent Research and Development

Autonomous Agents and Multi-Agent Systems

Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration

CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a Distributed Data Mining technique based on a multiagent environment, called SMAMDD (MultiAgent System for Distributed Data Mining), which uses model integration. Model Integration consists in the amalgamation of local models into a global, consistent one. In each subset, agents perform mining tasks locally and, afterwards, results are merged into a global model. In order to achieve that, agents cooperate by exchanging messages, aiming to improve the process of knowledge discover generating accurate results. The multiagent system for Distributed Data Mining proposed in this paper has been compared with classical machine learning algorithms which are based on model integration as well, simulating a distributed environment. The results obtained show that SMAMDD can produce highly accurate data models.