@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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        rdfs:label  "Institut für Data Science, Engineering, and Analytics" ;
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                <https://fis.th-koeln.de/vivo/individual/publ_11842> .

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        a           owl:Class ;
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        a             obo:BFO_0000001 , obo:BFO_0000020 , vivo:Authorship , vivo:Relationship , obo:BFO_0000002 , owl:Thing ;
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        rdfs:label                "RDF description of Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution - https://fis.th-koeln.de/vivo/individual/publ_11842" , "Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution" ;
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                <https://fis.th-koeln.de/vivo/individual/cite_11842-1> , <https://fis.th-koeln.de/vivo/individual/cite_11842-2> , <https://fis.th-koeln.de/vivo/individual/cite_11842-0> ;
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        bibo:abstract             "Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made by Artificial Intelligence (AI) systems in security-relevant applications. However, relatively little attention has been given to using these methods to improve the performance and robustness of deep learning algorithms. Additionally, much of the existing XAI work primarily addresses classification problems. In this study, we investigate the potential of feature attribution methods to filter out uninformative features in input data for regression problems, thereby improving the accuracy and stability of predictions. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery." ;
        bibo:doi                  "10.48550/arXiv.2409.16787" ;
        vitro:mostSpecificType    thkoeln:Journalartikel ;
        vivo:dateTimeValue        <http://cris.nrw/date2024> ;
        vivo:freetextKeyword      "XAI" , "Machine Learning (cs.LG)" , "FOS: Computer and information sciences" , "Artificial Intelligence (cs.AI)" , "Feature Attribution" , "Feature Selection" , "Deep Neural Network" , "Regression" , "Explainable Artificial Intelligence" ;
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        a       thkoeln:Zitat , owl:Thing .

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