Neural Computing and Applications

, Volume 31, Issue 10, pp 5679–5688 | Cite as

Construction biogeography-based optimization algorithm for solving classification problems

  • Mohammed AlweshahEmail author
Original Article


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.


Data mining Classification Optimization Metaheuristic Biogeography-based algorithm 


Compliance with ethical standards

Conflict of interest

The author certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Prince Abdullah Bin Ghazi Faculty of Information TechnologyAl-Balqa Applied UniversitySaltJordan

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