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Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics

Author

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  • Jui-Sheng Chou

    (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan)

  • Dinh-Nhat Truong

    (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
    Department of Civil Engineering, University of Architecture Ho Chi Minh City (UAH), Ho Chi Minh City 700000, Vietnam)

  • Chih-Fong Tsai

    (Department of Information Management, National Central University, Taoyuan City 320317, Taiwan)

Abstract

Machine learning techniques have been used to develop many regression models to make predictions based on experience and historical data. They might be used singly or in ensembles. Single models are either classification or regression models that use one technique, while ensemble models combine various single models. To construct or find the best model is very complex and time-consuming, so this study develops a new platform, called intelligent Machine Learner (iML), to automatically build popular models and identify the best one. The iML platform is benchmarked with WEKA by analyzing publicly available datasets. After that, four industrial experiments are conducted to evaluate the performance of iML. In all cases, the best models determined by iML are superior to prior studies in terms of accuracy and computation time. Thus, the iML is a powerful and efficient tool for solving regression problems in engineering informatics.

Suggested Citation

  • Jui-Sheng Chou & Dinh-Nhat Truong & Chih-Fong Tsai, 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics," Mathematics, MDPI, vol. 9(6), pages 1-25, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:686-:d:522358
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    References listed on IDEAS

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    1. Ganesan, P. & Rajakarunakaran, S. & Thirugnanasambandam, M. & Devaraj, D., 2015. "Artificial neural network model to predict the diesel electric generator performance and exhaust emissions," Energy, Elsevier, vol. 83(C), pages 115-124.
    2. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    3. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
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    1. Florin Leon & Mircea Hulea & Marius Gavrilescu, 2022. "Preface to the Special Issue on “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning”," Mathematics, MDPI, vol. 10(10), pages 1-4, May.

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