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DI2: prior-free and multi-item discretization of biological data and its applications
BACKGROUND: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumpt...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425008/ https://www.ncbi.nlm.nih.gov/pubmed/34496758 http://dx.doi.org/10.1186/s12859-021-04329-8 |
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author | Alexandre, Leonardo Costa, Rafael S. Henriques, Rui |
author_facet | Alexandre, Leonardo Costa, Rafael S. Henriques, Rui |
author_sort | Alexandre, Leonardo |
collection | PubMed |
description | BACKGROUND: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. RESULTS: In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. CONCLUSIONS: This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04329-8. |
format | Online Article Text |
id | pubmed-8425008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84250082021-09-10 DI2: prior-free and multi-item discretization of biological data and its applications Alexandre, Leonardo Costa, Rafael S. Henriques, Rui BMC Bioinformatics Software BACKGROUND: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations. RESULTS: In this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values. CONCLUSIONS: This work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2 SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04329-8. BioMed Central 2021-09-08 /pmc/articles/PMC8425008/ /pubmed/34496758 http://dx.doi.org/10.1186/s12859-021-04329-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Alexandre, Leonardo Costa, Rafael S. Henriques, Rui DI2: prior-free and multi-item discretization of biological data and its applications |
title | DI2: prior-free and multi-item discretization of biological data and its applications |
title_full | DI2: prior-free and multi-item discretization of biological data and its applications |
title_fullStr | DI2: prior-free and multi-item discretization of biological data and its applications |
title_full_unstemmed | DI2: prior-free and multi-item discretization of biological data and its applications |
title_short | DI2: prior-free and multi-item discretization of biological data and its applications |
title_sort | di2: prior-free and multi-item discretization of biological data and its applications |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425008/ https://www.ncbi.nlm.nih.gov/pubmed/34496758 http://dx.doi.org/10.1186/s12859-021-04329-8 |
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