A modified artificial bee colony algorithm for classification optimisation

Abstract

The promising capabilities, easily implementable and customisable structures of the meta-heuristic algorithms have increased the researchers’ attentions to the well-known problems and their new approximations that are suitable to be solved with the meta-heuristics directly. In this study, an attempt was made to solve with an artificial bee colony (ABC)-based technique called classifierABC algorithm, a new approximation that defines the classification problem by using a set of linear equations. The performance of the classifierABC was investigated in detail by using various datasets and assigning different values to the algorithm specific control parameters. The results obtained by the classifierABC algorithm were also compared with the results of the other meta-heuristics including particle swarm optimisation (PSO), differential evaluation (DE), fireworks algorithm (FWA) and different variants of the FWA. Comparative studies showed that the classifierABC solves the new problem approximation more robustly and its solutions determine the classes of instances in sets with high accuracies.