WATER EVAPORATION ALGORITHM WITH PROBABILISTIC NEURAL NETWORK FOR SOLVING CLASSIFICATION PROBLEMS

(Received: 23-Aug.-2019, Revised: 18-Oct.-2019 , Accepted: 3-Nov.-2019)
Classification is a crucial step in data mining as it facilitates decision-making in many areas of human activity, such as scientific endeavors, marketing campaigns, biomedical research and industrial applications. The probabilistic neural network (PNN) is widely utilized to solve classification and pattern recognition problems and is considered an effective method for solving such problems. In this paper, we propose an improved PNN model that employs the water evaporation algorithm (WEA) in order to solve classification problems more efficiently. The proposed method is able to obtain classification accuracies that are close to each other across all 11 benchmark tested datasets from the UCI machine-learning repository, which demonstrates the validity of this method (with respect to classification accuracy). The results show that the WEA is better than the firefly algorithm (FA) and biogeography-based optimization (BBO) in terms of both classification accuracy and convergence speed.
  1. A. Kaveh and T. Bakhshpoori, "Water Evaporation Optimization: A Novel Physically Inspired Optimization Algorithm," Computers & Structures, vol. 167, pp. 69-85, 2016.

  2. D. Li and Y. Du, Artificial Intelligence with Uncertainty, CRC press, 2017.

  3. F. Gorunescu, Data Mining : Concepts, Models and Techniques, Berlin: Springer, 2011.

  4. H. Faris, I. Aljarah and S. Mirjalili, "Training Feedforward Neural Networks Using Multi-verse Optimizer for Binary Classification Problems," Applied Intelligence, vol. 45, pp. 322-332, 2016.

  5. O. Adwan, H. Faris, K. Jaradat, O. Harfoushi and N. Ghatasheh, "Predicting Customer Churn in Telecom Industry Using Multilayer Preceptron Neural Networks: Modeling and Analysis," Life Science Journal, vol. 11, pp. 75-81, 2014.

  6. R. L. Schalock, S. A. Borthwick-Duffy, V. J. Bradley, W. H. Buntinx, D. L. Coulter, E. M. Craig, S. C. Gomez, Y. Lachapelle, R. Luckasson and A. Reeve, Intellectual Disability: Definition, Classification and Systems of Supports, ERIC, 2010.

  7. T. R. Kiran and S. Rajput, "An Effectiveness Model for An Indirect Evaporative Cooling (IEC) System: Comparison of Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Inference System (FIS) Approach," Applied Soft Computing, vol. 11, pp. 3525-3533, 2011.

  8. Z. Yang, Z. I. Rauen and C. Liu, "Automatic Tuning on Many-Core Platform for Energy Efficiency via Support Vector Machine Enhanced Differential Evolution," Scalable Computing: Practice and Experience, vol. 18, pp. 117-132, 2017.

  9. N. Friedman, D. Geiger and M. Goldszmidt, "Bayesian Network Classifiers," Machine learning, vol. 29, pp. 131-163, 1997.

  10. A. Alshareef, S. Ahmida, A. A. Bakar, A. R. Hamdan and M. Alweshah, "Mining Survey Data on University Students to Determine Trends in the Selection of Majors," Proc. of the Science and Information Conference (SAI), pp. 586-590, 2015.

  11. A. Alshareef, A. Alkilany, M. Alweshah and A. A. Bakar, "Toward a Student Information System for Sebha University, Libya," Proc. of the 5th International Conference on Innovative Computing Technology (INTECH), pp. 34-39, 2015.

  12. W.-H. Au and K. C. Chan, "Classification with Degree of Membership: A Fuzzy Approach," Proceedings of IEEE International Conference on Data Mining ( ICDM 2001), pp. 35-42, 2001.

  13. E. M. Azoff, Neural Network Time Series Forecasting of Financial Markets, John Wiley & Sons, Inc., 1994.

  14. M. Alweshah, A. I. Hammouri, H. Rashaideh, M. Ababneh and H. Tayyeb, "Solving Time Series Classification Problems Using Combined of Support Vector Machine and Neural Network," International Journal of Data Analysis Techniques and Strategies, vol. 9, 2017.

  15. M. Alweshah, A. I. Hammouri and S. Tedmori, "Biogeography-based Optimization for Data Classification Problems," International Journal of Data Mining, Modelling and Management, vol. 9, pp. 142-162, 2017.

  16. M. Alweshah, M. Abu Qadoura, A. I. Hammouri, M. S. Azmi and S. Alkhalaileh, "Flower Pollination Algorithm for Solving Classification Problems," International Journal of Advances in Soft Computing and Its Applications, In Press, pp. 1-13, 2019.

  17. M. Alweshah, "Firefly Algorithm with Artificial Neural Network for Time Series Problems," Research Journal of Applied Sciences, Engineering and Technology, vol. 7, pp. 3978-3982, 2014.

  18. I. Aljarah, H. Faris and S. Mirjalili, "Optimizing Connection Weights in Neural Networks Using the Whale Optimization Algorithm," Soft Computing, vol. 22, pp. 1-15, 2018.

  19. D. F. Specht, "Probabilistic Neural Networks," Neural networks, vol. 3, pp. 109-118, 1990.

  20. M. Alweshah, "Construction Biogeography-based Optimization Algorithm for Solving Classification Problems," Neural Computing and Applications, pp. 1-10, 2018.

  21. E.-G. Talbi, Metaheuristics: From Design to Implementation, John Wiley & Sons, 2009.

  22. Tareq Aziz AL-Qutami, I. I. Rosdiazli Ibrahim and M. A. Ishak, "Virtual Multiphase Flow Metering Ryalat, M. Almi’ani and A. I. Hammouri. Using Diverse Neural Network Ensemble and Adaptive Simulated Annealing," Expert Systems with Applications, vol. 93, pp. 72-85, 2017.

  23. F. Glover and M. Laguna, "Tabu Search∗," Handbook of Combinatorial Optimization, Ed., Springer, , pp. 3261-3362, 2013.

  24. A. Slowik and M. Bialko, "Training of Artificial Neural Networks using Differential Evolution Algorithm," Proc. of the Conference on Human System Interactions, pp. 60-65, 2008.

  25. J. Kennedy, "Particle Swarm Optimization," Encyclopedia of Machine Learning, Ed., Springer, pp. 760-766, 2011.

  26. X.-S. Yang, Nature-inspired Metaheuristic Algorithms, Luniver Press, 2010.

  27. D. J. Montana and L. Davis, "Training Feedforward Neural Networks Using Genetic Algorithms," IJCAI, pp. 762-767, 1989.

  28. X. S. Yang, "Firefly Algorithm," Engineering Optimization, pp. 221-230, 2010.

  29. R. Ak, Y. Li, V. Vitelli, E. Zio, E. L. Droguett and C. M. C. Jacinto, "NSGA-II-trained Neural Network Approach to the Estimation of Prediction Intervals of Scale Deposition Rate in Oil & Gas Equipment," Expert Systems with Applications, vol. 40, pp. 1205-1212, 2013.

  30. Z. Hongwu, Z. Jinya, L. Yan and Y. Chun, "Multi-objective Optimization of Helico-axial Multiphase Pump Impeller Based on NSGA-II," Proc. of the 2nd International Conference on Intelligent Computation Technology and Automation, pp. 202-205, 2009.

  31. E. Solgi, S. M. M. Husseini, A. Ahmadi and H. Gitinavard, "A Hybrid Hierarchical Soft Computing Approach for the Technology Selection Problem in Brick Industry Considering Environmental Competencies: A Case Study," Journal of Environmental Management, vol. 248, p. 109219, 2019.

  32. D. Whitley, T. Starkweather and C. Bogart, "Genetic Algorithms and Neural Networks: Optimizing Connections and Connectivity," Parallel Computing, vol. 14, pp. 347-361, 1990.

  33. K. Z. Mao, K.-C. Tan and W. Ser, "Probabilistic Neural-network Structure Determination for Pattern Classification," IEEE Transactions on Neural Networks, vol. 11, pp. 1009-1016, 2000.

  34. V. Kwigizile, M. F. Selekwa and R. N. Mussa, "Highway Vehicle Classification by Probabilistic Neural Networks," Proc. of the FLAIRS Conference, pp. 664-669, 2004.

  35. W. P. Sweeney Jr, M. T. Musavi and J. N. Guidi, "Classification of Cromosomes Using a Probabilistic Neural Network," Cytometry: The Journal of the International Society for Analytical Cytology, vol. 16, pp. 17-24, 1994. 

  36. M. Alweshah and S. Abdullah, "Hybridizing Firefly Algorithms with a Probabilistic Neural Network for Solving Classification Problems," Applied Soft Computing, vol. 35, pp. 513-524, 2015.

  37. P. Wasserman, Advanced Methods in Neural Networks: van Nostrand Reinhold, 1993.

  38. A. Saha, P. Das and A. K. Chakraborty, "Water Evaporation Algorithm: A New Metaheuristic Algorithm Towards the Solution of Optimal Power Flow," Engineering Science and Technology, an International Journal, vol. 20, pp. 1540-1552, 2017.

  39. M. Sokolova and G. Lapalme, "A Systematic Analysis of Performance Measures for Classification Tasks," Information Processing & Management, vol. 45, pp. 427-437, 2009.

  40. W. Mendenhall III, R. J. Beaver and B. M. Beaver, Introduction to Probability and Statistics: Cengage Learning, 2013.