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

Olivér Hornyák and László Barna Iantovics ()
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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

Mathematics, 2023, vol. 11, issue 8, 1-24

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.

Keywords: mathematical modeling; statistical analysis; statistical correlation; statistical significance; statistical analysis of experimental evaluation data; AdaBoost algorithm; smart applications; sensor data; failure data; condition-based maintenance; performance analysis of an algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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