<?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>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202023000300325</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[El enfoque de aprendizaje conjunto en la detección de fallas en cajas de engranajes]]></article-title>
<article-title xml:lang="en"><![CDATA[The joint learning approach to gearbox failure detection]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Mora]]></surname>
<given-names><![CDATA[Félix Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Escobar Chávez]]></surname>
<given-names><![CDATA[José Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gallegos Londoño]]></surname>
<given-names><![CDATA[César Marcelo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández Dávila]]></surname>
<given-names><![CDATA[Eduardo Segundo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Superior Politécnica de Chimborazo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>15</volume>
<numero>3</numero>
<fpage>325</fpage>
<lpage>333</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202023000300325&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202023000300325&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202023000300325&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El aprendizaje conjunto es un término que se utiliza para referirse a los métodos que combinan varios algoritmos para tomar una decisión, generalmente en tareas de aprendizaje automático supervisado. En los últimos años la regresión logística, Support Vector Machine, la red neuronal y otros algoritmos de aprendizaje de máquinas se han utilizado en el diagnostico inteligente de fallas en maquinaria rotativa, sin embargo, cada algoritmo tiene sus ventajas y desventajas y pueden funcionar de distinta manera para un determinado conjunto de datos. En el presente artículo científico se proponen tres modelos de detección de fallas en cajas de engranajes bajo el enfoque de aprendizaje conjunto utilizando las técnicas de clasificación por votación mayoritaria, votación suave y apilamiento a través de las cuales se combinan las predicciones de varios estimadores a fin de mejorar la generalización y la robustez sobre un solo estimador. Para el desarrollo de los modelos especificados se utiliza un conjunto de datos de señales de falla de cajas de engranajes y cuatro técnicas de clasificación las cuales son: regresión logística, support vector machine, XGBoost y random forest. Los resultados finales de precisión de los modelos de votación mayoritaria y votación suave son 99.86% y 99.82% respectivamente, mientras que la precisión del modelo bajo el enfoque de apilamiento es de 99.85%. En comparación con los resultados de los clasificadores individuales, se demuestra que los modelos de aprendizaje conjunto compensan los errores cometidos por los clasificadores individuales y mejoran efectivamente la precisión de clasificación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Joint learning is a term used to refer to methods that combine several algorithms to make a decision, usually in supervised machine learning tasks. In recent years logistic regression, Support Vector Machine, neural network and other machine learning algorithms have been used in intelligent fault diagnosis in rotating machinery, however, each algorithm has its advantages and disadvantages and may perform differently for a given data set. In this scientific paper, three gearbox fault detection models are proposed under the ensemble learning approach using the majority voting, soft voting and stacking classification techniques through which the predictions of several estimators are combined in order to improve generalization and robustness over a single estimator. A data set of gearbox failure signals and four classification techniques are used to develop the specified models: logistic regression, support vector machine, XGBoost and random forest. The final accuracy results of the majority voting and soft voting models are 99.86% and 99.82% respectively, while the accuracy of the model under the stacking approach is 99.85%. Compared with the results of the individual classifiers, it is shown that the ensemble learning models compensate for the errors made by the individual classifiers and effectively improve the classification accuracy.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje conjunto]]></kwd>
<kwd lng="es"><![CDATA[Votación mayoritaria]]></kwd>
<kwd lng="es"><![CDATA[Votación suave]]></kwd>
<kwd lng="es"><![CDATA[Apilamiento]]></kwd>
<kwd lng="es"><![CDATA[Caja de engranajes]]></kwd>
<kwd lng="en"><![CDATA[Joint learning]]></kwd>
<kwd lng="en"><![CDATA[Majority voting]]></kwd>
<kwd lng="en"><![CDATA[Soft voting]]></kwd>
<kwd lng="en"><![CDATA[Stacking]]></kwd>
<kwd lng="en"><![CDATA[Preventive maintenance]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Azamfar]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Bravo-Imaz]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis]]></article-title>
<source><![CDATA[Artificial Intelligence in Engineering]]></source>
<year>2021</year>
<page-range>713-21</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Barandas]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Folgado]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Fernandes]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Santos]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Abreu]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Bota]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Gamboa]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[TSFEL: Time series feature extraction library]]></article-title>
<source><![CDATA[SoftwareX]]></source>
<year>2020</year>
<volume>11</volume>
<page-range>100456</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Kwong]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Kong]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Class-specific soft voting based multiple extreme learning machines ensemble]]></article-title>
<source><![CDATA[Neurocomputing]]></source>
<year>2015</year>
<volume>149</volume>
<page-range>275-84</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Devendiran]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Manivannan]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Vibration based condition monitoring and fault diagnosis technologies for bearing and gear components-a review]]></article-title>
<source><![CDATA[International Journal of Applied Engineering Research]]></source>
<year>2016</year>
<volume>11</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>3966-75</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dong]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Cao]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey on ensemble learning]]></article-title>
<source><![CDATA[Frontiers of Computer Science]]></source>
<year>2020</year>
<volume>14</volume>
<page-range>241-58</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[García Mora]]></surname>
<given-names><![CDATA[F. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Davila]]></surname>
<given-names><![CDATA[E. H.]]></given-names>
</name>
<name>
<surname><![CDATA[Villa]]></surname>
<given-names><![CDATA[J. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Clasificación de fallas en rodamientos utilizando aprendizaje de máquinas]]></article-title>
<source><![CDATA[Dominio de las Ciencias]]></source>
<year>2021</year>
<volume>7</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>70-89</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Han]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhao]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Yin]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery]]></article-title>
<source><![CDATA[Transactions of the Institute of Measurement and Control]]></source>
<year>2018</year>
<volume>40</volume>
<numero>8</numero>
<issue>8</issue>
<page-range>2681-93</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Osman]]></surname>
<given-names><![CDATA[A. I. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmed]]></surname>
<given-names><![CDATA[A. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Chow]]></surname>
<given-names><![CDATA[M. F.]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Y. F.]]></given-names>
</name>
<name>
<surname><![CDATA[El-Shafie]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia]]></article-title>
<source><![CDATA[Ain Shams Engineering Journal]]></source>
<year>2021</year>
<volume>12</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>1545-56</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ibrahim]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Abdulazeez]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The role of machine learning algorithms for diagnosing diseases]]></article-title>
<source><![CDATA[Journal of Applied Science and Technology Trends]]></source>
<year>2021</year>
<volume>2</volume>
<numero>01</numero>
<issue>01</issue>
<page-range>10-9</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jones]]></surname>
<given-names><![CDATA[I. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Van Oyen]]></surname>
<given-names><![CDATA[M. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Lavieri]]></surname>
<given-names><![CDATA[M. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Andrews]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Stein]]></surname>
<given-names><![CDATA[J. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering]]></article-title>
<source><![CDATA[Health Care Management Science]]></source>
<year>2021</year>
<volume>24</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>686-701</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kumar]]></surname>
<given-names><![CDATA[U. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Nikhil]]></surname>
<given-names><![CDATA[M. B. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Sumangali]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<source><![CDATA[1-vote-Naïve Bayes SMV J48]]></source>
<year>2017</year>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lei]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Jia]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Nandi]]></surname>
<given-names><![CDATA[A. K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Applications of machine learning to machine fault diagnosis: A review and roadmap]]></article-title>
<source><![CDATA[Mechanical Systems and Signal Processing]]></source>
<year>2021</year>
<volume>138</volume>
<page-range>106587</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mahabub]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A robust technique of fake news detection using Ensemble Voting Classifier and comparison with other classifiers]]></article-title>
<source><![CDATA[SN Applied Sciences]]></source>
<year>2020</year>
<volume>2</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>525</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Patil]]></surname>
<given-names><![CDATA[P. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Patil]]></surname>
<given-names><![CDATA[M. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Tamhankar]]></surname>
<given-names><![CDATA[S. G.]]></given-names>
</name>
<name>
<surname><![CDATA[Patil]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Kazi]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Majority Voting Machine Learning Approach for Fault Diagnosis of Mechanical Components]]></article-title>
<source><![CDATA[Applications of Artificial Intelligence in Engineering : Proceedings of First Global Conference on Artificial Intelligence and Applications (GCAIA 2020)]]></source>
<year>2021</year>
<page-range>713-21</page-range><publisher-name><![CDATA[Springer Singapore]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sagi]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Rokach]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ensemble learning: A survey]]></article-title>
<source><![CDATA[Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery]]></source>
<year>2018</year>
<volume>8</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sarajcev]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Kunac]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Petrovic]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Despalatovic]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Power system transient stability assessment using stacked autoencoder and voting ensemble]]></article-title>
<source><![CDATA[Energies]]></source>
<year>2021</year>
<volume>14</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>3148</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Hao]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A comparative assessment of ensemble learning for credit scoring]]></article-title>
<source><![CDATA[Expert systems with applications]]></source>
<year>2011</year>
<volume>38</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>223-30</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wuest]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Weimer]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Irgens]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Thoben]]></surname>
<given-names><![CDATA[K. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning in manufacturing: advantages, challenges, and applications]]></article-title>
<source><![CDATA[Production &amp; Manufacturing Research]]></source>
<year>2016</year>
<volume>4</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>23-45</page-range></nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Qiu]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting]]></article-title>
<source><![CDATA[Bioinformatics]]></source>
<year>2020</year>
<volume>36</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>1074-81</page-range></nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[Z. H.]]></given-names>
</name>
</person-group>
<source><![CDATA[Ensemble methods: foundations and algorithms]]></source>
<year>2012</year>
<publisher-name><![CDATA[CRC press]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
