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

  • Abstract

    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.

     

     

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  • 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

    Received date: 2019-01-20

    Accepted date: 2019-01-20

    Published date: 2018-11-30