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A New Separation Index and Classification Techniques Based on Shannon Entropy

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dc.contributor.authorNavarro, Jorge
dc.contributor.authorBuono, Francesco
dc.contributor.authorMartín Arevalillo, Jorge
dc.contributor.orcidhttps://orcid.org/0000-0003-2822-915X
dc.contributor.orcidhttps://orcid.org/0000-0002-3569-4052
dc.date.accessioned2025-03-19T08:12:24Z
dc.date.available2025-03-19T08:12:24Z
dc.date.issued2023-09-22
dc.descriptionThe registered version of this article, first published in “Methodology and Computing in Applied Probability , vol. 25, 2023", is available online at the publisher's website: Springer, https://doi.org/10.1007/s11009-023-10055-w La versión registrada de este artículo, publicado por primera vez en “Methodology and Computing in Applied Probability , vol. 25, 2023", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s11009-023-10055-w
dc.description.abstractThe purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well.en
dc.description.versionversión final
dc.identifier.citationNavarro, J., Buono, F. & Arevalillo, J.M. A New Separation Index and Classification Techniques Based on Shannon Entropy. Methodol Comput Appl Probab 25, 78 (2023). https://doi.org/10.1007/s11009-023-10055-w
dc.identifier.doihttps://doi.org/10.1007/s11009-023-10055-w
dc.identifier.issn1387-5841 | eISSN 1573-7713
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26318
dc.journal.issue78
dc.journal.titleMethodology and Computing in Applied Probability
dc.journal.volume25
dc.language.isoen
dc.publisherSpringer
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsshannon entropyen
dc.subject.keywordsdiscriminant analysisen
dc.subject.keywordscluster analysisen
dc.subject.keywordskernel density estimationen
dc.subject.keywordsomic dataen
dc.titleA New Separation Index and Classification Techniques Based on Shannon Entropyen
dc.typeartículoes
dc.typejournal articleen
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relation.isAuthorOfPublication.latestForDiscoveryea1c092a-eceb-49c8-abb7-d4d52f53930a
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