@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              "RDF description of A Comparative Analysis of Concept Drift Detection Methods with a Systematic and Innovative Approach of Method Selection - https://fis.th-koeln.de/vivo/individual/publ_13328" , "A Comparative Analysis of Concept Drift Detection Methods with a Systematic and Innovative Approach of Method Selection" ;
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        thkoeln:sprache         "englisch" ;
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        bibo:abstract           "One of the mostsignificant challengesin data-driven modeling of complex systems   is dealing with concept drift, i.e., the unpredictable changes in the underlying data   distribution over time. In this work, nine concept drift detection (CDD) methods are   evaluated with respect to different types of concept drift, including abrupt, gradual,   incremental, and real concept drift, for both supervised and unsupervised application   scenarios. For the supervised case, the methods EDDM, FHDDMSadd, MDDM-E, and   EFDT are compared against a classification without change detection using Naïve   Bayes as the base classifier. In the unsupervised application scenario, CluStream,   ClusTree, DenStream, StreamKM++, and D-Stream are evaluated. The experiments are   conducted using the Massive Online Analysis (MOA) evaluation platform, and the   performance of each method is measured in terms of classification accuracy, memory   consumption, and computation time. This empirical research showsthat classification   accuracy can be improved by 20% by implementing a CDD method, highlighting the   importance of CDD in SHM data streams. However, there is no single method that   proves to be superior in all scenarios, and the choice depends on the characteristics of   the considered data stream and application requirements. Selecting the appropriate CDD   method from the approximately 340 different methods found in the literature is not a   trivial task and can lead to suboptimal selection. To tackle this issue, an innovative   approach is proposed to assist researchers and practitioners find the appropriate CCD   method for their application." ;
        bibo:isbn13             "9781605956930" , "#defaultkonferenzband" ;
        bibo:pageEnd            "1578" ;
        bibo:pageStart          "1571" ;
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