<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1390-8618</journal-id>
<journal-title><![CDATA[RETOS. Revista de Ciencias de la Administración y Economía]]></journal-title>
<abbrev-journal-title><![CDATA[Retos]]></abbrev-journal-title>
<issn>1390-8618</issn>
<publisher>
<publisher-name><![CDATA[Universidad Politécnica Salesiana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-86182023000200273</article-id>
<article-id pub-id-type="doi">10.17163/ret.n26.2023.06</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Optimización de hiperparámetros de regresión del proceso gaussiano para predecir problemas financieros]]></article-title>
<article-title xml:lang="en"><![CDATA[Gaussian Process Regressions Hyperparameters Optimization to Predict Financial Distress]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sabek]]></surname>
<given-names><![CDATA[Amine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Horák]]></surname>
<given-names><![CDATA[Jakub]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Tamanrasset  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Argelia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto de Tecnología y Empresas de &#268;eské Budejovice  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>República Checa</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>13</volume>
<numero>26</numero>
<fpage>273</fpage>
<lpage>289</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-86182023000200273&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-86182023000200273&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-86182023000200273&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: la predicción de las dificultades financieras se ha convertido en uno de los temas más importantes en el área contable y financiera debido a su correlación significativa con el desarrollo de la ciencia y la tecnología. El objetivo principal de este trabajo es predecir la dificultad financiera con base en la Regresión de Procesos Gaussianos (GPR) y luego comparar los resultados de este modelo con los resultados de otros modelos de aprendizaje profundo (SVM, LR, LD, DT, KNN). El análisis se basa en un conjunto de datos de 352 empresas extraídos de la base de datos de Kaggle. En cuanto a los predictores, se utilizaron 83 ratios financieros. El estudio concluyó que el uso de la GPR logra resultados muy relevantes. Además, superó al resto de los modelos de aprendizaje profundo y logró el primer lugar por igual con el modelo SVM con una precisión de clasificación del 81 %. Los resultados contribuyen al mantenimiento del sistema integrado y a la prosperidad de la economía del país, a la predicción de las dificultades financieras de las empresas y, por lo tanto, a la posible prevención de perturbaciones del sistema en cuestión.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology. The main objective of this paper is to predict financial distress based on the Gaussian Process Regression (GPR) and then compare the results of this model with the results of other deep learning models (SVM, LR, LD, DT, KNN). The analysis is based on a dataset of 352 companies extracted from the Kaggle database. As for predictors, 83 financial ratios were used. The study concluded that the use of GPR achieves very relevant results. Furthermore, it outperformed the rest of the deep learning models and achieved first place equally with the SVM model with a classification accuracy of 81%. The results contribute to the maintenance of the integrated system and the prosperity of the country&#8217;s economy, the prediction of the financial distress of companies and thus the potential prevention of disruption of the given system.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[dificultades financieras]]></kwd>
<kwd lng="es"><![CDATA[regresión del proceso gaussiano]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[financiamiento de inversiones]]></kwd>
<kwd lng="es"><![CDATA[predicción del riesgo financiero]]></kwd>
<kwd lng="es"><![CDATA[regresión gaussiana]]></kwd>
<kwd lng="es"><![CDATA[coeficientes financieros]]></kwd>
<kwd lng="es"><![CDATA[modelos de aprendizaje profundo]]></kwd>
<kwd lng="en"><![CDATA[financial distress]]></kwd>
<kwd lng="en"><![CDATA[Gaussian process regression]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[investment financing]]></kwd>
<kwd lng="en"><![CDATA[financial risk prediction]]></kwd>
<kwd lng="en"><![CDATA[Gaussian regression]]></kwd>
<kwd lng="en"><![CDATA[financial ratios]]></kwd>
<kwd lng="en"><![CDATA[deep learning models]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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