Abstract
DC* (Double Clustering with A*) is an algorithm capable of generating highly interpretable fuzzy information granules from pre-classified 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.
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Lucarelli, M., Mencar, C., Castiello, C., Fanelli, A.M. (2013). A New Heuristic Function for DC*. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_5
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DOI: https://doi.org/10.1007/978-3-319-03200-9_5
Publisher Name: Springer, Cham
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