A Market-based Rule Learning System

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.

• Type: Conference
• Year: 2004
• Where: ACSW
• Authors: Qingqing Zhou, Martin K. Purvis
• Keywords: ACSW 2004, ACSW 2007, Better Performance Rules, Classifier System-inspired Model, Rule Inference Performance