ITERATIVE INDUCTION OF A CATEGORY MEMBERSHIP FUNCTION
Hiroshi Narazaki, Anca Ralescu · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 1994
We propose a new iterative method for inducing classification knowledge from symbolic data. Our method generates a hierarchy of clusters and does not assume a particular knowledge representation formula (e.g., a conjunctive formula, a linear discriminant function). Our method consists of two stages, i.e., the optimization and clustering stages. The first stage maps the symbolic problem into the numerical domain based on an optimization approach. In the second stage, the examples are clustered into positive, negative, and fuzzy zones using induced membership degrees. This learning procedure is iterated until the fuzzy zone becomes empty. Out method learns "topological knowledge" which is found to be useful for the evaluation of the training data quality. Further, we show that our method is useful using real world data of an industrial knowledge acquisition problem.