Biogeography-based rule mining for classification

Rule-based classification is a popular approach for solving real world classification problems. Once suitable rules have been obtained, rule-based classifiers are easy to deploy and explain. In this paper, we describe an approach that uses biogeography-based optimization (BBO) to compute rule sets that maximize predictive accuracy. BBO is an evolutionary algorithm inspired by the migration patterns of species between the islands of an archipelago. In our implementation, each species corresponds to a classification rule, each island is occupied by multiple species and corresponds to a classifier, and the fitness of an island is computed as the predictive classification accuracy of the corresponding classifier. The archipelago evolves via mutation, selection, and migration of species between islands. Successful islands have a decreased immigration rate and an increased emigration rate. In general, such islands tend to resist invasion and to colonize less successful islands. This results in an evolving set of habitats that corresponds to a population of classifiers. We demonstrate the effectiveness of our approach by comparing it to several traditional and evolutionary based state-of-the-art classifiers.