<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000301063</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-3-4951</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Evaluating the Impact of Removing Low-relevance Features in Non-retrained Neural Networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Corona-Bermúdez]]></surname>
<given-names><![CDATA[Uriel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Menchaca-Méndez]]></surname>
<given-names><![CDATA[Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Menchaca-Méndez]]></surname>
<given-names><![CDATA[Rolando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Corona-Bermúdez]]></surname>
<given-names><![CDATA[Erendira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Centro de Investigación en Computación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>3</numero>
<fpage>1063</fpage>
<lpage>1075</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000301063&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000301063&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000301063&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Feature selection is a widely used technique to boost the efficiency of machine learning models, particularly when working with high-dimensional datasets. However, after reducing the feature space, we must retrain the model to measure the impact of the removed features. This can be inconvenient, especially when dealing with large datasets of thousands or millions of instances, as it leads to computationally expensive processes. To avoid the costly procedure of retraining, this study evaluates the impact of predicting using neural networks that have not been retrained after feature selection. We used two architectures that allow feature removal without affecting the architectural structure: FT-Transformers, which are capable of generating predictions even when certain features are excluded from the input, and Multi-layer Perceptrons, by pruning unused weights. These methods are compared against XGBoost, which requires retraining, on various tabular datasets. Our experiments demonstrate that the proposed approaches achieve competitive performance compared to retrained models, especially when the removal percentage is up to 20%. Notably, the proposed methods exhibit significantly faster evaluation times, particularly on large datasets. These methods offer a promising solution for efficiently applying feature removals, providing a favorable trade-off between performance and computational costs.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Feature selection]]></kwd>
<kwd lng="en"><![CDATA[transformers]]></kwd>
<kwd lng="en"><![CDATA[pruning models]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ba]]></surname>
<given-names><![CDATA[J. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Kiros]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Hinton]]></surname>
<given-names><![CDATA[G. E.]]></given-names>
</name>
</person-group>
<source><![CDATA[Layer normalization]]></source>
<year>2016</year>
</nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bee-Wah]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Ibrahim]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Abdul-Hamid]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Abdul-Rahman]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Simon]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feature selection methods: Case of filter and wrapper approaches for maximising classification accuracy]]></article-title>
<source><![CDATA[Pertanika Journal of Science and Technology]]></source>
<year>2018</year>
<volume>26</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>329-40</page-range></nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Casalicchio]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Bossek]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lang]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Kirchhoff]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Kerschke]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Hofner]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Seibold]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Vanschoren]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Bischl]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[OpenML: An R package to connect to the machine learning platform OpenML]]></article-title>
<source><![CDATA[Computational Statistics]]></source>
<year>2017</year>
<volume>34</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>977-91</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Guestrin]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<source><![CDATA[XGBoost: A scalable tree boosting system]]></source>
<year>2016</year>
<conf-name><![CDATA[ 22nd Association for Computing Machinery&#8217;s Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining]]></conf-name>
<conf-loc> </conf-loc>
<page-range>785-94</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Devlin]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Chang]]></surname>
<given-names><![CDATA[M. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Toutanova]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<source><![CDATA[BERT: Pre-training of deep bidirectional transformers for language understanding]]></source>
<year>2018</year>
<volume>1</volume>
<conf-name><![CDATA[ Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies]]></conf-name>
<conf-loc> </conf-loc>
<page-range>4171-86</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ghosh]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Guha]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Sarkar]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Abraham]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A wrapper-filter feature selection technique based on ant colony optimization]]></article-title>
<source><![CDATA[Neural Computing and Applications]]></source>
<year>2019</year>
<volume>32</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>7839-57</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gorishniy]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Rubachev]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Khrulkov]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Babenko]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Revisiting deep learning models for tabular data]]></source>
<year>2021</year>
<conf-name><![CDATA[ 35th Conference on Neural Information Processing System]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-25</page-range></nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Guyon]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Elisseeff]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An introduction of variable and feature selection]]></article-title>
<source><![CDATA[The Journal of Machine Learning Research]]></source>
<year>2003</year>
<volume>3</volume>
<page-range>1157-82</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ho]]></surname>
<given-names><![CDATA[T. K.]]></given-names>
</name>
</person-group>
<source><![CDATA[Random decision forests]]></source>
<year>1995</year>
<volume>1</volume>
<conf-name><![CDATA[ 3rd International Conference on Document Analysis and Recognition]]></conf-name>
<conf-loc> </conf-loc>
<page-range>278-82</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hollmann]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Müller]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Eggensperger]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Hutter]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<source><![CDATA[TabPFN: A transformer that solves small tabular classification problems in a second]]></source>
<year>2022</year>
<conf-name><![CDATA[ International Conference on Learning Representations]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-37</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Khetan]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cvitkovic]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Karnin]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<source><![CDATA[TabTransformer: Tabular data modeling using contextual embeddings]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Inza]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Larrañaga]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Blanco]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Cerrolaza]]></surname>
<given-names><![CDATA[A. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Filter versus wrapper gene selection approaches in DNA microarray domains]]></article-title>
<source><![CDATA[Artificial Intelligence in Medicine]]></source>
<year>2004</year>
<volume>31</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>91-103</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kadra]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Lindauer]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Hutter]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Grabocka]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Well-tuned simple nets excel on tabular datasets]]></source>
<year>2021</year>
<conf-name><![CDATA[ 35th Conference on Neural Information Processing Systems]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1-14</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kuhn]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Johnson]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<source><![CDATA[Applied Predictive Modeling]]></source>
<year>2013</year>
<publisher-name><![CDATA[Springer New York]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Morey]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Customer data: Designing for transparency and trust]]></article-title>
<source><![CDATA[Harvard Business Review]]></source>
<year>2015</year>
<volume>May. 2015</volume>
<page-range>96-105</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pedregosa]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Varoquaux]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Gramfort]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Michel]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Thirion]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Grisel]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Blondel]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Prettenhofer]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Weiss]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Dubourg]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
<name>
<surname><![CDATA[Vanderplas]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Passos]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cournapeau]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Brucher]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Perrot]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Duchesnay]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Scikit-learn: Machine learning in Python]]></article-title>
<source><![CDATA[Journal of Machine Learning Research]]></source>
<year>2011</year>
<volume>12</volume>
<numero>85</numero>
<issue>85</issue>
<page-range>2825-30</page-range></nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pudjihartono]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Fadason]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Kempa-Liehr]]></surname>
<given-names><![CDATA[A. W.]]></given-names>
</name>
<name>
<surname><![CDATA[O&#8217;Sullivan]]></surname>
<given-names><![CDATA[J. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A review of feature selection methods for machine learning-based disease risk prediction]]></article-title>
<source><![CDATA[Frontiers in Bioinformatics]]></source>
<year>2022</year>
<volume>2</volume>
</nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Russell]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Norvig]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<source><![CDATA[Artificial Intelligence: A Modern Approach]]></source>
<year>2010</year>
<publisher-name><![CDATA[Prentice Hall]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vaswani]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Shazeer]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Parmar]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Uszkoreit]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Jones]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Gomez]]></surname>
<given-names><![CDATA[A. N.]]></given-names>
</name>
<name>
<surname><![CDATA[Kaiser]]></surname>
<given-names><![CDATA[&#321;.]]></given-names>
</name>
<name>
<surname><![CDATA[Polosukhin]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<source><![CDATA[Attention is all you need]]></source>
<year>2017</year>
<volume>30</volume>
<conf-name><![CDATA[ Advances in Neural Information Processing Systems]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Venkat]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<source><![CDATA[The curse of dimensionality: Inside out]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Xiao]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Wong]]></surname>
<given-names><![CDATA[D. F.]]></given-names>
</name>
<name>
<surname><![CDATA[Chao]]></surname>
<given-names><![CDATA[L. S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Learning deep transformer models for machine translation]]></source>
<year>2019</year>
<conf-name><![CDATA[ 57th Annual Meeting of the Association for Computational Linguistics]]></conf-name>
<conf-loc> </conf-loc>
<page-range>1810-22</page-range></nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wooldridge]]></surname>
<given-names><![CDATA[J. M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Introductory econometrics: A modern approach]]></source>
<year>2013</year>
<publisher-name><![CDATA[South-Western]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Ensemble machine learning: Methods and applications]]></source>
<year>2012</year>
<publisher-name><![CDATA[Springer Publishing Company, Incorporated]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
