Static and dynamic feature weighting in case-based reasoning (CBR)
Zhong Zhang · Library and Archives Canada (Government of Canada) · 1997
Case-based reasoning(CBR) is a recent approach to problem solving, in which domain knowledge is represented as cases.The case retrieval process, which retrieves the cases most similar to the new problem, depends on the feature-value pairs attached to cases.Different feature-value pairs may have different importance in this process, which is usually measured by what we call the feature weight.Three serious problems arise in the practical applications of CBR regarding the feature weights.First, the feature weights are assigned manually by humans, not only making them highly informal and inaccurate, but also involving intensive labor.Second, a CBR system with a static set of feature weights cannot cater to a specific user.It would be desirable to enable the system to acquire the user preferences automatically.Finally, a CBR system often functions in a changing environment, either due to the nature of the problems it is trying to solve, or due to the shifting needs of its user.We wish to have a CBR system that always adapts to the user's changing preferences in time.These three problems comprise one of the core tasks of case base maintenance problem.. . . . . . . . .