Abstract
Chiu et al. recently published an article entitled Developing a case-based reasoning system of leisure in volume 10 of the Information Technology Journal. In this article, they applied the case-based reasoning technology to predicting the type of leisure constraints that visitors encounter to assist leisure services providers in negotiation of the constraints. Unfortunately, the inference relies on all instances in the database and features of these instances (or cases). Inaccurate results may be obtained if the instances or features are not representative. A plan of simultaneously selecting representative instances and features through GA optimized training with random combinations of instances or features is proposed and expected to improve the performance of the existing system.
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