A prototype-based classification using extended imperialist competitive algorithm

Classi cation is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an ecient prototypebased classi cation approach in the data classi cation literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classi er is
called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three di erent 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 classi cation including original Imperialist Competitive Algorithm (ICA), the Arti cial 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 classi cation techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classi cation techniques.