AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics
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 c...
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| Format: | Article |
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MDPI AG
2023-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/8/1801 |
| _version_ | 1797604480184745984 |
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| author | Olivér Hornyák László Barna Iantovics |
| author_facet | Olivér Hornyák László Barna Iantovics |
| author_sort | Olivér Hornyák |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:47:02Z |
| format | Article |
| id | doaj.art-c13c416edb3e49278c22acfba7ca2c06 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T04:47:02Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c13c416edb3e49278c22acfba7ca2c062023-11-17T20:16:38ZengMDPI AGMathematics2227-73902023-04-01118180110.3390/math11081801AdaBoost Algorithm Could Lead to Weak Results for Data with Certain CharacteristicsOlivér Hornyák0László Barna Iantovics1Institute of Information Engineering, University of Miskolc, 3515 Miskolc, HungaryDepartment of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, RomaniaThere 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.https://www.mdpi.com/2227-7390/11/8/1801mathematical modelingstatistical analysisstatistical correlationstatistical significancestatistical analysis of experimental evaluation dataAdaBoost algorithm |
| spellingShingle | Olivér Hornyák László Barna Iantovics AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics Mathematics mathematical modeling statistical analysis statistical correlation statistical significance statistical analysis of experimental evaluation data AdaBoost algorithm |
| title | AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics |
| title_full | AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics |
| title_fullStr | AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics |
| title_full_unstemmed | AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics |
| title_short | AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics |
| title_sort | adaboost algorithm could lead to weak results for data with certain characteristics |
| topic | mathematical modeling statistical analysis statistical correlation statistical significance statistical analysis of experimental evaluation data AdaBoost algorithm |
| url | https://www.mdpi.com/2227-7390/11/8/1801 |
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