{"id":"https://openalex.org/W4293250578","doi":"https://doi.org/10.1007/s41060-022-00350-z","title":"DeepTLF: robust deep neural networks for heterogeneous tabular data","display_name":"DeepTLF: robust deep neural networks for heterogeneous tabular data","publication_year":2022,"publication_date":"2022-08-23","ids":{"openalex":"https://openalex.org/W4293250578","doi":"https://doi.org/10.1007/s41060-022-00350-z"},"language":"en","primary_location":{"id":"doi:10.1007/s41060-022-00350-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-022-00350-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-022-00350-z.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s41060-022-00350-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078188012","display_name":"Vadim Borisov","orcid":"https://orcid.org/0000-0002-4889-9989"},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Vadim Borisov","raw_affiliation_strings":["The University of T\u00fcbingen, T\u00fcbingen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4889-9989","affiliations":[{"raw_affiliation_string":"The University of T\u00fcbingen, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I8087733"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066050663","display_name":"Klaus Broelemann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klaus Broelemann","raw_affiliation_strings":["SCHUFA Holding AG, Wiesbaden, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SCHUFA Holding AG, Wiesbaden, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008809634","display_name":"Enkelejda Kasneci","orcid":"https://orcid.org/0000-0003-3146-4484"},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Enkelejda Kasneci","raw_affiliation_strings":["The University of T\u00fcbingen, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of T\u00fcbingen, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I8087733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024434748","display_name":"Gjergji Kasneci","orcid":null},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gjergji Kasneci","raw_affiliation_strings":["SCHUFA Holding AG, Wiesbaden, Germany","The University of T\u00fcbingen, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SCHUFA Holding AG, Wiesbaden, Germany","institution_ids":[]},{"raw_affiliation_string":"The University of T\u00fcbingen, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I8087733"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5078188012"],"corresponding_institution_ids":["https://openalex.org/I8087733"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":4.1631,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.94682635,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"16","issue":"1","first_page":"85","last_page":"100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8390210270881653},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7204852104187012},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6722312569618225},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6350533962249756},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5805870890617371},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5630436539649963},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5455942749977112},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5259880423545837},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.506133496761322},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.4783191978931427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4730231761932373},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3654635548591614}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8390210270881653},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7204852104187012},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6722312569618225},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6350533962249756},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5805870890617371},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5630436539649963},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5455942749977112},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5259880423545837},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.506133496761322},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4783191978931427},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4730231761932373},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3654635548591614},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s41060-022-00350-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-022-00350-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-022-00350-z.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s41060-022-00350-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-022-00350-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-022-00350-z.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321112","display_name":"Eberhard Karls Universit\u00e4t T\u00fcbingen","ror":"https://ror.org/03a1kwz48"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4293250578.pdf","grobid_xml":"https://content.openalex.org/works/W4293250578.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W575847903","https://openalex.org/W2056132907","https://openalex.org/W2059424674","https://openalex.org/W2070493638","https://openalex.org/W2076618162","https://openalex.org/W2104170135","https://openalex.org/W2167277498","https://openalex.org/W2295598076","https://openalex.org/W2295739661","https://openalex.org/W2296719434","https://openalex.org/W2342408547","https://openalex.org/W2581465409","https://openalex.org/W2604662567","https://openalex.org/W2622755869","https://openalex.org/W2743948853","https://openalex.org/W2793768763","https://openalex.org/W2911964244","https://openalex.org/W2950445386","https://openalex.org/W2950858167","https://openalex.org/W3013460382","https://openalex.org/W3020873385","https://openalex.org/W3035231859","https://openalex.org/W3104030692","https://openalex.org/W3114632476","https://openalex.org/W3159500102","https://openalex.org/W3160590016","https://openalex.org/W3161605597","https://openalex.org/W3170989320","https://openalex.org/W3202428668","https://openalex.org/W3204266507","https://openalex.org/W3216660278","https://openalex.org/W4256561644","https://openalex.org/W4313164293","https://openalex.org/W6600085209","https://openalex.org/W6600605178","https://openalex.org/W6602696911","https://openalex.org/W6675354045","https://openalex.org/W6744330852","https://openalex.org/W6818324960"],"related_works":["https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703","https://openalex.org/W41015297","https://openalex.org/W4280645561"],"abstract_inverted_index":{"Abstract":[0],"Although":[1],"deep":[2,55,67],"neural":[3],"networks":[4],"(DNNs)":[5],"constitute":[6],"the":[7,10,39,42,52,79,88,94,108,115,121,135,139,145,148,179],"state":[8],"of":[9,33,44,54,73,90,110,147],"art":[11],"in":[12,195],"many":[13],"tasks":[14],"based":[15],"on":[16,24,114,172],"visual,":[17],"audio,":[18],"or":[19],"text":[20],"data,":[21],"their":[22],"performance":[23,89,193],"heterogeneous,":[25],"tabular":[26,48,68],"data":[27,49,82,85,118,163],"is":[28,76,153,202],"typically":[29],"inferior":[30],"to":[31,46,77,86,119,133,155,183,197],"that":[32,128,178],"decision":[34,111,157],"tree":[35,158],"ensembles.":[36],"To":[37],"bridge":[38],"gap":[40],"between":[41],"difficulty":[43],"DNNs":[45,91],"handle":[47],"and":[50,169],"leverage":[51],"flexibility":[53],"learning":[56],"under":[57],"input":[58,81,123],"heterogeneity,":[59],"we":[60,97,142,176],"propose":[61],"DeepTLF":[62,180,200],",":[63,105],"a":[64,99,129],"framework":[65,190],"for":[66],"learning.":[69],"The":[70,199],"core":[71],"idea":[72],"our":[74,189],"method":[75],"transform":[78],"heterogeneous":[80,117],"into":[83],"homogeneous":[84,126],"boost":[87],"considerably.":[92],"For":[93],"transformation":[95],"step,":[96],"develop":[98],"novel":[100],"knowledge":[101],"distillations":[102],"approach,":[103],"TreeDrivenEncoder":[104],"which":[106],"exploits":[107],"structure":[109],"trees":[112],"trained":[113],"available":[116,204],"map":[120],"original":[122],"vectors":[124,127],"onto":[125],"DNN":[130],"can":[131],"use":[132],"improve":[134],"predictive":[136,185],"performance.":[137,186],"Within":[138],"proposed":[140],"framework,":[141],"also":[143],"address":[144],"issue":[146],"multimodal":[149],"learning,":[150],"since":[151],"it":[152],"challenging":[154,170],"apply":[156],"ensemble":[159],"methods":[160],"when":[161],"other":[162],"modalities":[164],"are":[165],"present.":[166],"Through":[167],"extensive":[168],"experiments":[171],"various":[173],"real-world":[174],"datasets,":[175],"demonstrate":[177],"pipeline":[181],"leads":[182],"higher":[184],"On":[187],"average,":[188],"shows":[191],"19.6%":[192],"improvement":[194],"comparison":[196],"DNNs.":[198],"code":[201],"publicly":[203],".":[205]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-02T09:04:35.204637","created_date":"2025-10-10T00:00:00"}
