A New Heuristic Function for DC

摘要

DC~* (Double Clustering with A~*) is an algorithm capable of generating highly interpretable fuzzy information granules from preclassified data. These information granules can be used as bulding-blocks for fuzzy rule-based classifiers that exhibit a good tradeoff between interpretability and accuracy. DC~* relies on A~* for the granulation process, whose efficiency is tightly related to the heuristic function used for estimating the costs of candidate solutions. In this paper we propose a new heuristic function that is capable of exploiting class information to overcome the heuristic function originally used in DC~* in terms of efficiency. The experimental results show that the proposed heuristic function allows huge savings in terms of computational effort, thus making DC~* a competitive choice for designing interpretable fuzzy rule-based classifiers.