Construction biogeography-based optimization algorithm for solving classification problems

Abstract

Classification is a data mining task that assigns items in a collection to predefined categories or classes, also referred to as supervised learning. The goal of classification is to accurately predict the target class for each case in the data. A review of the literature shows that many algorithms, including statistical and machine learning algorithms, have been successfully used to handle classification problems in different areas, but their performance varies considerably. Even though the neural network is effective in addressing a wide range of problems, to date no specific neural network approach has been found that can ensure that the optimal solution is arrived at when solving classification problems. Some of the important challenges include finding the most appropriate weight parameter for the classifier through the implementation of population-based approaches; attaining a balance between the processes of exploration and exploitation by employing hybridization methods; and obtaining fast convergence by controlling random movement and by generating good initial solutions. This study investigates how can good initial populations drive higher convergence speed and better classification accuracy in solving classification problems. Local search (in this case, the simulated annealing algorithm) is used to produce an initial solution for the classification problem and then a heuristic initialization hybridized with biogeography-based optimization is applied. The proposed approaches are tested on 11 standard benchmark datasets. This is a new approach in the classification arena, and it represents an approach that outperforms the current state of the art on most of the tested benchmark datasets.