A market-based rule learning system

Authors:
QingQing Zhou;Martin Purvis
Affiliations:
GuangDong Data Communication Bureau, China Telecom;University of Otago, Dunedin, New Zealand
Venue:
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Year:
2004

Citing 4
Cited 0

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
C4.5: programs for machine learning

C4.5: programs for machine learning
Market-based control: a paradigm for distributed resource allocation

Market-based control: a paradigm for distributed resource allocation
Classifier fitness based on accuracy

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a 'market trading' technique is integrated with the techniques of rule discovery and refinement for data mining. A classifier system-inspired model, the market-based rule learning (MBRL) system is proposed and its capability of evolving and refining rules is investigated. Experimental results indicate that the MBRL system is a potentially useful additional tool that can be used to refine neural network extracted rules and possibly discover and add some new, better performance rules. As a result, it can lead to improved performance by increasing the accuracy of the rule inference performance and/or improving the comprehensibility of the rules.