BENCHMARKING BIO-INSPIRED COMPUTATION ALGORITHMS AS WRAPPERS FOR FEATURE SELECTION
Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of computational cost but also of overall quality. Wrapper-based procedures are widely utilised to achieve this. The choice of the wrapper is of utmost importance. Bio-inspired...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2020-08-01
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| Series: | Acta Electrotechnica et Informatica |
| Subjects: | |
| Online Access: | http://www.aei.tuke.sk/papers/2020/2/05_Bajer.pdf |
| Summary: | Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of
computational cost but also of overall quality. Wrapper-based procedures are widely utilised to achieve this. The choice of the wrapper
is of utmost importance. Bio-inspired computation algorithms represent a viable choice and are widely adopted. Due to the sheer
number of available algorithms, this choice could prove to be somewhat difficult, especially since not all are made equally. The
aim of this paper is to explore several optimisers on diverse datasets representing classification problems in order to evaluate their
performance and suitability for the task of feature selection |
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| ISSN: | 1335-8243 1338-3957 |