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 from 45% to 99%.
Efficiency Improvement of DC* through a Genetic Guidance
CASTIELLO, CIRO;MENCAR, CORRADO;
2017-01-01
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 from 45% to 99%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
