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Feature-Based Complexity Measure for Multinomial Classification Datasets
Machine learning algorithms are frequently used for classification problems on tabular datasets. In order to make informed decisions about model selection and design, it is crucial to gain meaningful insights into the complexity of these datasets. Feature-based complexity measures are a set of compl...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378522/ https://www.ncbi.nlm.nih.gov/pubmed/37509947 http://dx.doi.org/10.3390/e25071000 |
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author | Erwin, Kyle Engelbrecht, Andries |
author_facet | Erwin, Kyle Engelbrecht, Andries |
author_sort | Erwin, Kyle |
collection | PubMed |
description | Machine learning algorithms are frequently used for classification problems on tabular datasets. In order to make informed decisions about model selection and design, it is crucial to gain meaningful insights into the complexity of these datasets. Feature-based complexity measures are a set of complexity measures that evaluates how useful features are at discriminating instances of different classes. This paper, however, shows that existing feature-based measures are inadequate in accurately measuring the complexity of various synthetic classification datasets, particularly those with multiple classes. This paper proposes a new feature-based complexity measure called the F5 measure, which evaluates the discriminative power of features for each class by identifying long sequences of uninterrupted instances of the same class. It is shown that the F5 measure better represents the feature complexity of a dataset. |
format | Online Article Text |
id | pubmed-10378522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103785222023-07-29 Feature-Based Complexity Measure for Multinomial Classification Datasets Erwin, Kyle Engelbrecht, Andries Entropy (Basel) Article Machine learning algorithms are frequently used for classification problems on tabular datasets. In order to make informed decisions about model selection and design, it is crucial to gain meaningful insights into the complexity of these datasets. Feature-based complexity measures are a set of complexity measures that evaluates how useful features are at discriminating instances of different classes. This paper, however, shows that existing feature-based measures are inadequate in accurately measuring the complexity of various synthetic classification datasets, particularly those with multiple classes. This paper proposes a new feature-based complexity measure called the F5 measure, which evaluates the discriminative power of features for each class by identifying long sequences of uninterrupted instances of the same class. It is shown that the F5 measure better represents the feature complexity of a dataset. MDPI 2023-06-29 /pmc/articles/PMC10378522/ /pubmed/37509947 http://dx.doi.org/10.3390/e25071000 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Erwin, Kyle Engelbrecht, Andries Feature-Based Complexity Measure for Multinomial Classification Datasets |
title | Feature-Based Complexity Measure for Multinomial Classification Datasets |
title_full | Feature-Based Complexity Measure for Multinomial Classification Datasets |
title_fullStr | Feature-Based Complexity Measure for Multinomial Classification Datasets |
title_full_unstemmed | Feature-Based Complexity Measure for Multinomial Classification Datasets |
title_short | Feature-Based Complexity Measure for Multinomial Classification Datasets |
title_sort | feature-based complexity measure for multinomial classification datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378522/ https://www.ncbi.nlm.nih.gov/pubmed/37509947 http://dx.doi.org/10.3390/e25071000 |
work_keys_str_mv | AT erwinkyle featurebasedcomplexitymeasureformultinomialclassificationdatasets AT engelbrechtandries featurebasedcomplexitymeasureformultinomialclassificationdatasets |