A prototype-based classifi cation 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 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 classication including original Imperialist Competitive Algorithm (ICA), the Articial Bee Colony (ABC), the Fire y 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