شماره ركورد كنفرانس :
144
عنوان مقاله :
Fuzzy Rule Weights Optimization based on Imperialist Competitive Algorithm
پديدآورندگان :
Rezaei Mansoureh نويسنده , Boostani Reza نويسنده
كليدواژه :
FRBCSs , GA , Imperialist Competitive Algorithm (ICA) , PSO
عنوان كنفرانس :
مجموعه مقالات دوازدهمين كنفرانس سيستم هاي هوشمند ايران
چكيده فارسي :
Fuzzy rule-based systems are appropriate tools to deal
with the classification problems due to their interpretabilities and
accuracies. The aim of the paper is to improve the performance
of Fuzzy Rule-Based Classification Systems (FRBCS) by learning
their weights using Imperialist Competitive Algorithm (ICA).
Among the evolutionary algorithms, here, ICA is chosen to solve
the premature convergence problem of the other competitive
algorithms. To evaluate the proposed method, several datasets
belonged to the UCI database are selected as the benchmark and
applied to the proposed FRBCS optimized by ICA and finally
compared to the other FRBCS which their weights are adjusted
by other evolutionary algorithms such as Genetic Algorithm
(GA) and Particle Swarm Optimization (PSO). The achieved
results on most of the datasets imply on the superiority of the
proposed combinational scheme compared to the other similar
rivals.
شماره مدرك كنفرانس :
3817034