A prototype-based classification using extended imperialist competitive algorithm

Abstract

Classification is an important machine learning technique used to predict roup membership for data instances. In this paper, we propose an efficient prototypebased classification approach in the data classification literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classifier is called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three different fitness 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 classification including original Imperialist Competitive Algorithm (ICA), the Artificial Bee Colony (ABC), the Fire fly Algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), the Grouping Gravitational Search Algorithm (GGSA), and nine well-known classification techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classification techniques.