Advertisement

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

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.

Keywords

Data mining Classification Optimization Metaheuristic Biogeography-based algorithm 

Notes

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.

References

  1. 1.
    Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576CrossRefGoogle Scholar
  2. 2.
    Alweshah M, Omar A, Alzubi J, Alaqeel S (2016) Solving attribute reduction problem using wrapper genetic programming. Int J Comput Sci Netw Secur 16:77–84Google Scholar
  3. 3.
    Warwick K, Craddock R (1996) An introduction to radial basis functions for system identification. A comparison with other neural network methods. In: Proceedings of the 35th IEEE conference on decision and control, vol 1, pp 464–469Google Scholar
  4. 4.
    Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163CrossRefGoogle Scholar
  5. 5.
    Alshareef AM, Bakar AA, Hamdan AR, Abdullah SMS, Alweshah M (2015) A case-based reasoning approach for pattern detection in Malaysia rainfall data. Int J Big Data Intell 2:285–302CrossRefGoogle Scholar
  6. 6.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Elsevier, AmsterdamzbMATHGoogle Scholar
  7. 7.
    Alweshah M, Hammouri AI, Rashaideh H, Ababneh M, Tayyeb H (2017) Solving time series classification problems using combined of support vector machine and neural network. In: International journal of data analysis techniques and strategies (in press) Google Scholar
  8. 8.
    Alshareef A, Ahmida S, Bakar AA, Hamdan AR, Alweshah M (2015) Mining survey data on university students to determine trends in the selection of majors. Sci Inf Conf (SAI) 2015:586–590CrossRefGoogle Scholar
  9. 9.
    Ren K, Qu J (2014) Identification of shaft centerline orbit for wind power units based on Hopfield neural network improved by simulated annealing. In: Mathematical problems in engineeringGoogle Scholar
  10. 10.
    El-Bouri A (2012) An investigation of initial solutions on the performance of an iterated local search algorithm for the permutation flowshop. In: 2012 IEEE congress on evolutionary computation (CEC), pp 1–5Google Scholar
  11. 11.
    Syed Mustafa A, Kumara Swamy YS (2015) Web service classification using multi-layer perceptron optimized with Tabu search. In: 2015 IEEE international on advance computing conference (IACC), pp 290–294Google Scholar
  12. 12.
    Tairan N, Qingfu Z (2010) Population-based guided local search: some preliminary experimental results. In: 2010 IEEE congress on evolutionary computation (CEC), pp 1–5Google Scholar
  13. 13.
    Xie X, Huibo Z, Yanping L, Yongyue Z (2012) Variable neighborhood search based multi-objective dynamic crane scheduling. In: 2012 international conference on measurement, information and control (MIC), pp 457–460Google Scholar
  14. 14.
    Malinak P, Jaksa R (2007) Simultaneous gradient and evolutionary neural network weights adaptation methods. In: IEEE congress on evolutionary computation, CEC, pp 2665–2671Google Scholar
  15. 15.
    Ekkachai K, Nilkhamhang I (2016) Swing phase control of semi-active prosthetic knee using neural network predictive control with particle swarm optimization. IEEE Trans Neural Syst Rehabil Eng 24:1169CrossRefGoogle Scholar
  16. 16.
    Fei H, Dan Y, Qing-Hua L, De-Shuang H (2015) A novel diversity-guided ensemble of neural network based on attractive and repulsive particle swarm optimization. In: 2015 international joint conference neural networks (IJCNN), pp 1–7Google Scholar
  17. 17.
    Hu L, Qin L, Mao K, Chen W, Fu X (2016) Optimization of neural network by genetic algorithm for flowrate determination in multipath ultrasonic gas flowmeter. Sens J EEE 16:1158–1167Google Scholar
  18. 18.
    Dandan L, Wanxin X, Yilei P (2015) A high-precision prediction model using Ant Colony Algorithm and neural network, In: 2015 international conference on logistics, informatics and service sciences (LISS), pp 1–6Google Scholar
  19. 19.
    Ilkucar M, Isik AH, Cifci A (2014) Classification of breast cancer data with harmony search and back propagation based artificial neural network. In: 2014 22nd signal processing and communications applications conference (SIU), pp 762–765Google Scholar
  20. 20.
    Xiaojin X, Yun P, Ruijuan J, Yilan L (2015) Optimizing neural network classification by using the Cuckoo algorithm. In: 2015 11th international conference on natural computation (ICNC), pp 24–30Google Scholar
  21. 21.
    Mengmeng L, Li T, Li Z, Dabo Z (2014) A fault diagnosis algorithm using probabilistic neural network with particle fish swarm algorithm. In: 2014 11th World Congress on, intelligent control and automation (WCICA), pp 3713–3717Google Scholar
  22. 22.
    Alweshah M, Abdullah S (2015) Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl Soft Comput 35:513–524CrossRefGoogle Scholar
  23. 23.
    Alweshah M (2014) Firefly algorithm with artificial neural network for time series problems. Res J Appl Sci Eng Technol 7:3978–3982CrossRefGoogle Scholar
  24. 24.
    Alweshah M, Hammouri AI, Tedmori S (2017) Biogeography-based optimisation for data classification problems. Int J Data Min Modelling Manag 9:142–162Google Scholar
  25. 25.
    Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley, HobokenCrossRefGoogle Scholar
  26. 26.
    S. Sakamoto, E. Kulla, T. Oda, M. Ikeda, L. Barolli, and F. Xhafa, “A comparison study of Hill Climbing, Simulated Annealing and Genetic Algorithm for node placement problem in WMNs,” Journal of High Speed Networks, vol. 20, pp. 55-66, 01/01/2014Google Scholar
  27. 27.
    Guo W, Chen M, Wang L, Mao Y, Wu Q (2017) A survey of biogeography-based optimization. Neural Comput Appl 28:1909–1926CrossRefGoogle Scholar
  28. 28.
    Ammu P, Sivakumar K, Rejimoan R (2013) Biogeography-based optimization-a survey. Int J Electron Comput Sci Eng 2:154–160Google Scholar
  29. 29.
    Wang Z-C, Wu X-B (2014) Hybrid biogeography-based optimization for integer programming. Sci World JGoogle Scholar
  30. 30.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209MathSciNetCrossRefGoogle Scholar
  31. 31.
    Kirkpatrick S (1984) Optimization by simulated annealing: quantitative studies. J Stat Phys 34:975–986MathSciNetCrossRefGoogle Scholar
  32. 32.
    Rahnamayan S, Tizhoosh HR, Salama M (2007) A novel population initialization method for accelerating evolutionary algorithms. Comput Math Appl 53:1605–1614MathSciNetCrossRefGoogle Scholar
  33. 33.
    Rutkowski L, Cpalka K (2003) Flexible neuro-fuzzy systems. Neural Netw 14:554–574CrossRefGoogle Scholar
  34. 34.
    Polat K, Sahan S, Kodaz H, Günes S (2007) Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Syst Appl 32:172–183CrossRefGoogle Scholar
  35. 35.
    Mooney RJ (1996) Comparative experiments on disambiguating word senses: an illustration of the role of bias in machine learning. In: The computing research repository (CoRR), pp 82–91Google Scholar
  36. 36.
    Leon IV W (2006) Enhancing pattern classification with relational fuzzy neural networks and square BK-products. Ph.D. dissertation in computer science, Springer, FL, USAGoogle Scholar
  37. 37.
    Zarndt F (1995) A comprehensive case study: an examination of machine learning and connectionist algorithms. Ph.D., Department of Computer Science, Brigham Young UniversityGoogle Scholar
  38. 38.
    Ene M (2008) Neural network-based approach to discriminate healthy people from those with Parkinson’s diseaseGoogle Scholar
  39. 39.
    Pham HNA, Triantaphyllou E (2011) A meta-heuristic approach for improving the accuracy in some classification algorithms. Comput Oper Res 38:174–189MathSciNetCrossRefGoogle Scholar

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

Personalised recommendations