Comparison of Instance Selection and Construction Methods with Various Classifiers
Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/11/3933 |
| _version_ | 1797565994312400896 |
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| author | Marcin Blachnik Mirosław Kordos |
| author_facet | Marcin Blachnik Mirosław Kordos |
| author_sort | Marcin Blachnik |
| collection | DOAJ |
| description | Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the <i>Drop2</i> and <i>Drop3</i>. Other methods are less efficient or provide low compression ratio. |
| first_indexed | 2024-03-10T19:20:32Z |
| format | Article |
| id | doaj.art-2185eabea2674be9b68fca30d9f34fe9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:20:32Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2185eabea2674be9b68fca30d9f34fe92023-11-20T03:00:08ZengMDPI AGApplied Sciences2076-34172020-06-011011393310.3390/app10113933Comparison of Instance Selection and Construction Methods with Various ClassifiersMarcin Blachnik0Mirosław Kordos1Faculty of Materials Engineering, Department of Industrial Informatics, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, PolandDepartment of Computer Science, University of Bielsko-Biała, Willowa 2, 43-309 Bielsko-Biała, PolandInstance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the <i>Drop2</i> and <i>Drop3</i>. Other methods are less efficient or provide low compression ratio.https://www.mdpi.com/2076-3417/10/11/3933machine learningclassificationpreprocessinginstance selection |
| spellingShingle | Marcin Blachnik Mirosław Kordos Comparison of Instance Selection and Construction Methods with Various Classifiers Applied Sciences machine learning classification preprocessing instance selection |
| title | Comparison of Instance Selection and Construction Methods with Various Classifiers |
| title_full | Comparison of Instance Selection and Construction Methods with Various Classifiers |
| title_fullStr | Comparison of Instance Selection and Construction Methods with Various Classifiers |
| title_full_unstemmed | Comparison of Instance Selection and Construction Methods with Various Classifiers |
| title_short | Comparison of Instance Selection and Construction Methods with Various Classifiers |
| title_sort | comparison of instance selection and construction methods with various classifiers |
| topic | machine learning classification preprocessing instance selection |
| url | https://www.mdpi.com/2076-3417/10/11/3933 |
| work_keys_str_mv | AT marcinblachnik comparisonofinstanceselectionandconstructionmethodswithvariousclassifiers AT mirosławkordos comparisonofinstanceselectionandconstructionmethodswithvariousclassifiers |