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Open AccessArticle

Comparison of Instance Selection and Construction Methods with Various Classifiers

1
Faculty of Materials Engineering, Department of Industrial Informatics, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
2
Department of Computer Science, University of Bielsko-Biała, Willowa 2, 43-309 Bielsko-Biała, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(11), 3933; https://doi.org/10.3390/app10113933
Received: 7 May 2020 / Revised: 29 May 2020 / Accepted: 1 June 2020 / Published: 5 June 2020
(This article belongs to the Special Issue Applied Machine Learning)
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 Drop2 and Drop3. Other methods are less efficient or provide low compression ratio. View Full-Text
Keywords: machine learning; classification; preprocessing; instance selection machine learning; classification; preprocessing; instance selection
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Blachnik, M.; Kordos, M. Comparison of Instance Selection and Construction Methods with Various Classifiers. Appl. Sci. 2020, 10, 3933.

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