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AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics

Author

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  • Olivér Hornyák

    (Institute of Information Engineering, University of Miskolc, 3515 Miskolc, Hungary)

  • László Barna Iantovics

    (Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania)

Abstract

There are many state-of-the-art algorithms presented in the literature that perform very well on some evaluation data but are not studied with the data properties on which they are applied; therefore, they could have low performance on data with other characteristics. In this paper, the results of comprehensive research regarding the prediction with the frequently applied AdaBoost algorithm on real-world sensor data are presented. The chosen dataset has some specific characteristics, and it contains error and failure data of several machines and their components. The research aims to investigate whether the AdaBoost algorithm has the capability of predicting failures, thus providing the necessary information for monitoring and condition-based maintenance (CBM). The dataset is analyzed, and the principal characteristics are presented. Performance evaluations of the AdaBoost algorithm that we present show a prediction capability below expectations for this algorithm. The specificity of this study is that it indicates the limitation of the AdaBoost algorithm, which could perform very well on some data, but not so well on others. Based on this research and some others that we performed, and actual research from worldwide studies, we must outline that the mathematical analysis of the data is especially important to develop or adapt algorithms to be very efficient.

Suggested Citation

  • Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1801-:d:1120152
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    References listed on IDEAS

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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Florin Leon & Marius Gavrilescu, 2021. "A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving," Mathematics, MDPI, vol. 9(6), pages 1-37, March.
    3. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    4. Shuo Sun & Qianli Zhang & Junzhong Sun & Wei Cai & Zhiyong Zhou & Zhanlu Yang & Zongliang Wang, 2022. "Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm," Energies, MDPI, vol. 15(16), pages 1-20, August.
    5. Jung-Kai Tsai & Chih-Hsing Hung, 2021. "Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19," Mathematics, MDPI, vol. 9(18), pages 1-10, September.
    6. Wang, W. & Zhang, W., 2008. "An asset residual life prediction model based on expert judgments," European Journal of Operational Research, Elsevier, vol. 188(2), pages 496-505, July.
    7. Douglas Bonett & Thomas Wright, 2000. "Sample size requirements for estimating pearson, kendall and spearman correlations," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 23-28, March.
    8. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
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