Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy

  • Authors

    • N Harudin
    • Jamaludin K R
    • M Nabil Muhtazaruddin
    • Ramlie F
    • Wan Zuki Azman Wan Muhammad
    • NN Jaafar
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.35.26276
  • Artificial Bee colony, Feature selection, Orthogonal Array, Prediction, T-Method

  • The study of prediction has drawn great interest in a wide range of field. T-Method which was developed specifically for prediction of the multidimensional case using historical data to develop its baseline model proved that making a prediction is possible even with limited sample size. The element of the signal to noise ratio (SNR) adopted into the T-Method strengthens its robustness. Orthogonal array (OA) in T-Method was used as features selection optimization in improving the analysis speed, cost and computer burden during the analysis. However, the limitation of OA in dealing with higher dimensionality and complex combination factors restraint the optimization accuracy. Artificial Bee Colony (ABC) was adopted in this study to overcome this limitation. The result of this study shows that T-method +ABC provide the best error% accuracy with only 2.45% and 2.53% (3 optimized features out of 15) compared to T-Method +OA which 2.81% and 2.67% and T-Method +Spearman Correlation as 3.16% and 3.06%. The power consumption prediction case study is a good example for cases that deal with high correlation coefficient (R2) baseline model (>0.8). If the R2 is lower than 0.8, further enhancement needs to be done to ensure a low risk of high error% prediction.

     

     

  • References

    1. [1] J. Drgoňa, D. Picard, M. Kvasnica, and L. Helsen, “Approximate model predictive building control via machine learning,†Appl. Energy, vol. 218, pp. 199–216, May 2018.

      [2] J. Reynolds, Y. Rezgui, A. Kwan, and S. Piriou, “A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control,†Energy, vol. 151, pp. 729–739, May 2018.

      [3] S. Mohsen, Z. Yusop, J. Milad, and Y. Fadhilah, “Development of Generalized Feed Forward Network for Predicting Annual Flood ( Depth ) of a Tropical River,†Sains Malaysiana, vol. 43, no. 12, pp. 1865–1871, 2014.

      [4] S. Ding, K. W. Hipel, and Y. Dang, “Forecasting China’s electricity consumption using a new grey prediction model,†Energy, vol. 149, pp. 314–328, Apr. 2018.

      [5] N. H. A. Rahman, M. H. Lee, M. T. Latif, and Suhartono, “Evaluation Performance of Time Series Approach for Forecasting Air Pollution Index in Johor , Malaysia,†Sains Malaysiana, vol. 45, no. 11, pp. 1625–1633, 2016.

      [6] S. Teshima, Y. Hasegawa, and K. Tatebayashi, Quality Recognition and Prediction: Smarter Pattern Technology with the Mahalanobis-Taguchi System. New York: Momentum Press, 2012.

      [7] S. Negishi, Y. Morimoto, S. Takayama, and A. Ishigame, “Daily Peak Load Forecasting by Taguchi’s T Method,†Electr. Eng. Japan (English Transl. Denki Gakkai Ronbunshi), vol. 201, no. 1, pp. 57–65, 2017.

      [8] P. Dasneogi, E. Cudney, A. Adekpedjou, and R. Kestle, “Comparing the Predictive Ability of T-Method and Cobb-Douglas Production Function for Warranty Data,†in Proceedings of the ASME 2009 International Mechanical Engineering Congress & Exposition, 2015, pp. 1–6.

      [9] H. Kawada and Y. Nagata, “An application of a generalized inverse regression estimator to Taguchi ’ s T-Method,†J. Japanese Soc. Qual. Control, no. 1985, pp. 3–4, 2015.

      [10] J. Inoh, Y. Nagata, K. Horita, and M. Arisa, “PredictionAccuracies of lmproved Taguchi s T Methods Compared to those of MultipleRegression Analysis†J. japanese Soc. Qual. Control, vol. 42, no. 2, pp. 265–277, 2012.

      [11] E. A. Cudney and P. A. Shah, “Predicting Annual Precipitation Using the T-Method Missouri University of Science and Technology,†in Proceedings of the 2010 Industrial Engineering Research Conference, 2010.

      [12] W. H. Woodall, R. Koudelik, K. L. Tsui, S. B. Kim, Z. G. Stoumbos, and C. R. Carvounis, “A review and analysis of the Mahalanobis-Taguchi system,†Technometrics, vol. 45, no. 1, pp. 1–15, 2003.

      [13] B. Abraham and A. M. Variyath, “Discussion,†Technometrics, vol. 45, no. 1, pp. 22–24, 2003.

      [14] D. M. Hawkins, “Discussion,†Technometrics, vol. 45, no. 1, pp. 25–29, 2003.

      [15] S. Taguchi, R. Jugulum, G. Taguchi, and J. O. Wilkins, “Discussion,†Technometrics, vol. 45, no. 1, pp. 16–21, 2003.

      [16] K. Tsui, T. Sukchotrat, and V. C. P. Chen, “A comparison study and discussion of the Mahalanobis-Taguchi System Seoung Bum Kim,†Int. J. Ind. Syst. Eng., vol. 4, no. 6, pp. 631–644, 2009.

      [17] P. Saraiva, N. Faísca, R. Costa, and A. Gonçalves, “Fault identification in chemical processes through a modified mahalanobis-taguchi strategy,†Comput. Aided Chem. Eng., vol. 18, no. C, pp. 799–804, 2004.

      [18] C. R. Foster, R. Jugulum, and D. D. Frey, “Evaluating an adaptive One-Factor-At-a-Time search procedure within the Mahalanobis-Taguchi System,†Internatinal J. Ind. Syst. Eng., vol. 4, no. 6, pp. 600–614, 2009.

      [19] A. Pal and J. Maiti, “Development of a hybrid methodology for dimensionality reduction in Mahalanobis–Taguchi system using Mahalanobis distance and binary particle swarm optimization,†Expert Syst. Appl., vol. 37, no. 2, pp. 1286–1293, 2010.

      [20] E. Reséndiz and C. a. Rull-Flores, “Mahalanobis-Taguchi system applied to variable selection in automotive pedals components using Gompertz binary particle swarm optimization,†Expert Syst. Appl., vol. 40, no. 7, pp. 2361–2365, 2013.

      [21] E. Reséndiz, L. a. Moncayo-Martínez, and G. Solís, “Binary ant colony optimization applied to variable screening in the Mahalanobis-Taguchi System,†Expert Syst. Appl., vol. 40, no. 2, pp. 634–637, 2013.

      [22] A. S. Iquebal and A. Pal, “Artificial bee colony optimisation-based enhanced Mahalanobis Taguchi system for classification,†Int. J. Intell. Eng. Informatics, vol. 2, no. 2–3, pp. 1–14, 2014.

      [23] F. Ramlie, K. R. Jamaludin, R. Dolah, and W. Z. A. W. Muhamad, “Optimal feature selection of Taguchi character recognition in the Mahalanobis-Taguchi system using bees algorithm,†Glob. J. Pure Appl. Math., vol. 12, no. 3, pp. 2651–2671, 2016.

      [24] C. G. Mota-Gutiérrez, E. O. Reséndiz-Flores, and Y. I. Reyes-Carlos, “Mahalanobis-Taguchi system: state of the art,†Int. J. Qual. Reliab. Manag., vol. 35, no. 3, pp. 596–613, 2018.

      [25] E. O. Reséndiz-Flores and M. E. López-Quintero, “Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach,†Int. J. Adv. Manuf. Technol., vol. 90, no. 1–4, pp. 437–443, 2017.

      [26] A. L. Bolaji, A. T. Khader, M. A. Al-Betar, and M. A. Awadallah, “Artificial bee colony algorithm, its variants and applications: A survey,†J. Theor. Appl. Inf. Technol., vol. 47, no. 2, pp. 434–459, 2013.

      [27] E. Hancer, B. Xue, D. Karaboga, and M. Zhang, “A binary ABC algorithm based on advanced similarity scheme for feature selection,†Appl. Soft Comput. J., vol. 36, pp. 334–348, 2015.

      [28] F. G. Mohammadi and H. Sajedi, “Region based Image Steganalysis using Artificial Bee Colony,†J. Vis. Commun. Image Represent., vol. 44, pp. 214–226, 2017.

      [29] P. Shunmugapriya and S. Kanmani, “A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid),†Swarm Evol. Comput., vol. 36, no. February 2016, pp. 27–36, 2017.

      [30] E. Zorarpaci, S.A Ozel and S. Gungor, “An Artificial Bee Colony Based Algorithm for Feature Selection,†vol. 30, no. June, pp. 25–31, 2015.

      [31] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,†J. Glob. Optim., vol. 39, no. 3, pp. 459–471, 2007.

      [32] Mohammad Shokouhifar Shahid, “An Artificial Bee Colony Optimization for Feature Subset Selection using Supervised Fuzzy C-Means Algorithm,†in International Conference on Information Security and Artificial Intelligence (ISAI 2010), 2010, pp. 427–432.

      [33] M. S. Uzer, N. Yilmaz, O. Inan, M. S. Uzer, N. Yilmaz, and O. Inan, “Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification.,†ScientificWorldJournal., vol. 2013, p. 419187, 2013.

  • Downloads

  • How to Cite

    Harudin, N., K R, J., Nabil Muhtazaruddin, M., F, R., Zuki Azman Wan Muhammad, W., & Jaafar, N. (2018). Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy. International Journal of Engineering & Technology, 7(4.35), 885-891. https://doi.org/10.14419/ijet.v7i4.35.26276