Efficiency improvement of DC∗ through a Genetic Guidance

Abstract

DC∗ is a method for generating interpretable fuzzy information granules from pre-classified data. It is based on the subsequent application of LVQ1 for data compression and an ad-hoc procedure based on A∗ to represent data with the minimum number of fuzzy information granules satisfying some interpretability constraints. While being efficient in tackling several problems, the A∗ procedure included in DC∗ may happen to require a long computation time because the A∗ algorithm has exponential time complexity in the worst case. In this paper, we approach the problem of driving the search process of A∗ by suggesting a close-to-optimal solution that is produced through a Genetic Algorithm (GA). Experimental evaluations show that, by driving the A∗ algorithm embodied in DC∗ with a GA solution, the time required to perform data granulation can be reduced by at least 45% and up to 99%.