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Title: SYSTEMATIC LOOK AT MACHINE LEARNING ALGORITHMS ? ADVANTAGES, DISADVANTAGES AND PRACTICAL APPLICATIONS
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SYSTEMATIC LOOK AT MACHINE LEARNING ALGORITHMS ? ADVANTAGES, DISADVANTAGES AND PRACTICAL APPLICATIONS
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1314-2704
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English
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20
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2.1
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Machine Learning (ML) is the study and the usage of the mathematical algorithms
which can improve their performance without the need for human interaction. These algorithms are considered as a subset of Artificial Intelligence (AI). Machine learning algorithms use past data as input and produce new predicted values as an output. Machine learning algorithms have been used in many areas for solving an innumerable number of tasks. However, the various tasks need applying of different machine learning algorithms for obtaining maximum accuracy of the target results. In this paper, an analysis with consideration of the advantages, disadvantages, and different areas of applications in the real world are made for each of the four ML algorithm groups - supervised, unsupervised, semi-supervised, and reinforcement learning. After the comparative analysis is done, the ensemble methods boosting, stacking, and bagging are introduced, described, and compared. Emphasis is done on defining the accuracy of which ML algorithms can be improved and which ensemble methods can be used for that. Machine Learning algorithms combined with ensemble methods are highly competitive and provide the best results in most cases where they are applicable. |
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conference
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20th International Multidisciplinary Scientific GeoConference SGEM 2020
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20th International Multidisciplinary Scientific GeoConference SGEM 2020, 18 - 24 August, 2020
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts & Letters; Acad Fine Arts Zagreb Croatia; C
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317-324
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18 - 24 August, 2020
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website
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cdrom
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7002
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machine learning; ML algorithms; ensemble methods.
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