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.
|Cite as: Zhou, Q. and Purvis, M. (2004). A Market-based Rule Learning System. In Proc. Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand. CRPIT, 32. Purvis, M., Ed. ACS. 175-180. |
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