Document Type: Article
Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
Classication is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an ecient prototypebased classication approach in the data classication literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classier is
called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three dierent tness functions on twelve typical test datasets from the UCI Machine Learning Repository. The performance of the proposed EICA is compared with well-developed algorithms in classication including original Imperialist Competitive Algorithm (ICA), the Articial Bee Colony (ABC), the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), the Grouping Gravitational Search Algorithm (GGSA), and nine well-known classication techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classication techniques.