{"id":"https://openalex.org/W3180281962","doi":"https://doi.org/10.1007/s10994-021-06023-5","title":"Density-based weighting for imbalanced regression","display_name":"Density-based weighting for imbalanced regression","publication_year":2021,"publication_date":"2021-07-07","ids":{"openalex":"https://openalex.org/W3180281962","doi":"https://doi.org/10.1007/s10994-021-06023-5","mag":"3180281962"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-021-06023-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06023-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06023-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","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/s10994-021-06023-5.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087863012","display_name":"Michael Steininger","orcid":"https://orcid.org/0000-0002-3102-481X"},"institutions":[{"id":"https://openalex.org/I25974101","display_name":"University of W\u00fcrzburg","ror":"https://ror.org/00fbnyb24","country_code":"DE","type":"education","lineage":["https://openalex.org/I25974101"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Steininger","raw_affiliation_strings":["Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany"],"raw_orcid":"https://orcid.org/0000-0002-3102-481X","affiliations":[{"raw_affiliation_string":"Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany","institution_ids":["https://openalex.org/I25974101"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031390620","display_name":"Konstantin Kobs","orcid":null},"institutions":[{"id":"https://openalex.org/I25974101","display_name":"University of W\u00fcrzburg","ror":"https://ror.org/00fbnyb24","country_code":"DE","type":"education","lineage":["https://openalex.org/I25974101"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Konstantin Kobs","raw_affiliation_strings":["Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany","institution_ids":["https://openalex.org/I25974101"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034240062","display_name":"Padraig Davidson","orcid":"https://orcid.org/0009-0005-0689-8127"},"institutions":[{"id":"https://openalex.org/I25974101","display_name":"University of W\u00fcrzburg","ror":"https://ror.org/00fbnyb24","country_code":"DE","type":"education","lineage":["https://openalex.org/I25974101"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Padraig Davidson","raw_affiliation_strings":["Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany","institution_ids":["https://openalex.org/I25974101"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015079780","display_name":"Anna Krause","orcid":"https://orcid.org/0000-0003-1924-9183"},"institutions":[{"id":"https://openalex.org/I25974101","display_name":"University of W\u00fcrzburg","ror":"https://ror.org/00fbnyb24","country_code":"DE","type":"education","lineage":["https://openalex.org/I25974101"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Anna Krause","raw_affiliation_strings":["Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany","institution_ids":["https://openalex.org/I25974101"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054225034","display_name":"Andreas Hotho","orcid":"https://orcid.org/0000-0002-0483-5772"},"institutions":[{"id":"https://openalex.org/I25974101","display_name":"University of W\u00fcrzburg","ror":"https://ror.org/00fbnyb24","country_code":"DE","type":"education","lineage":["https://openalex.org/I25974101"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Hotho","raw_affiliation_strings":["Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Computer Science X, University of W\u00fcrzburg, W\u00fcrzburg, Germany","institution_ids":["https://openalex.org/I25974101"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5087863012"],"corresponding_institution_ids":["https://openalex.org/I25974101"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":12.1714,"has_fulltext":true,"cited_by_count":175,"citation_normalized_percentile":{"value":0.98881975,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"110","issue":"8","first_page":"2187","last_page":"2211"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9890999794006348,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7973110675811768},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6969218254089355},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6865976452827454},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6325357556343079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6018938422203064},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5888219475746155},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5379041433334351},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.49335184693336487},{"id":"https://openalex.org/keywords/rare-events","display_name":"Rare events","score":0.4388166666030884},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4146009087562561},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4117586612701416},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2675894498825073},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15630599856376648}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7973110675811768},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6969218254089355},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6865976452827454},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6325357556343079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6018938422203064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5888219475746155},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5379041433334351},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.49335184693336487},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.4388166666030884},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4146009087562561},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4117586612701416},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2675894498825073},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15630599856376648},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-021-06023-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06023-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06023-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:opus.bibliothek.uni-wuerzburg.de:26917","is_oa":true,"landing_page_url":"https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-269177","pdf_url":null,"source":{"id":"https://openalex.org/S4306401635","display_name":"Online Publication Service of W\u00fcrzburg University (W\u00fcrzburg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I25974101","host_organization_name":"University of W\u00fcrzburg","host_organization_lineage":["https://openalex.org/I25974101"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s10994-021-06023-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-06023-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-06023-5.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5699999928474426,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325356","display_name":"Julius-Maximilians-Universit\u00e4t W\u00fcrzburg","ror":"https://ror.org/00fbnyb24"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3180281962.pdf","grobid_xml":"https://content.openalex.org/works/W3180281962.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W70001371","https://openalex.org/W1529672735","https://openalex.org/W1561774098","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1966334841","https://openalex.org/W1972483847","https://openalex.org/W1975749451","https://openalex.org/W2015452969","https://openalex.org/W2104933073","https://openalex.org/W2129905273","https://openalex.org/W2148143831","https://openalex.org/W2335024046","https://openalex.org/W2338318698","https://openalex.org/W2440599146","https://openalex.org/W2490420619","https://openalex.org/W2604521289","https://openalex.org/W2753300133","https://openalex.org/W2772032254","https://openalex.org/W2775447965","https://openalex.org/W2951281886","https://openalex.org/W2963647337","https://openalex.org/W2963691377","https://openalex.org/W2991198822","https://openalex.org/W3083510345","https://openalex.org/W3111477757","https://openalex.org/W3139860299","https://openalex.org/W4233014035","https://openalex.org/W4244228474","https://openalex.org/W4252684946","https://openalex.org/W4254751698","https://openalex.org/W6654255728","https://openalex.org/W6744066916","https://openalex.org/W6758313303","https://openalex.org/W6910827590"],"related_works":["https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W2140186469","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W2775233965","https://openalex.org/W2180954594","https://openalex.org/W4318559728","https://openalex.org/W4360995913","https://openalex.org/W4312193868"],"abstract_inverted_index":{"Abstract":[0],"In":[1,93],"many":[2],"real":[3],"world":[4],"settings,":[5,57],"imbalanced":[6,103,118],"data":[7,119,129,145,156,166,183,244],"impedes":[8],"model":[9,161,179,213],"performance":[10,180],"of":[11,59,239],"learning":[12,79,111],"algorithms,":[13],"like":[14],"neural":[15,114],"networks,":[16],"mostly":[17,203],"for":[18,25,55,71,102,113,181,242],"rare":[19,30,155,182,230,243],"cases.":[20],"This":[21],"is":[22,81],"especially":[23],"problematic":[24],"tasks":[26],"focusing":[27,226],"on":[28,123,148,160,170,222,227],"these":[29],"occurrences.":[31],"For":[32],"example,":[33],"when":[34],"estimating":[35],"precipitation,":[36],"extreme":[37],"rainfall":[38],"events":[39],"are":[40,50],"scarce":[41],"but":[42],"important":[43],"considering":[44],"their":[45,133],"potential":[46],"consequences.":[47],"While":[48],"there":[49],"numerous":[51],"well":[52],"studied":[53],"solutions":[54,70],"classification":[56,91],"most":[58],"them":[60],"cannot":[61],"be":[62],"applied":[63],"to":[64,83,87,132,152,164,194,219],"regression":[65,72,104,116],"easily.":[66],"Of":[67],"the":[68,149,195,223,237],"few":[69],"tasks,":[73],"barely":[74],"any":[75],"have":[76,84],"explored":[77],"cost-sensitive":[78,110],"which":[80],"known":[82],"advantages":[85],"compared":[86,163],"sampling-based":[88],"methods":[89],"in":[90],"tasks.":[92],"this":[94],"work,":[95],"we":[96,191],"propose":[97],"a":[98,109,233],"sample":[99],"weighting":[100,125,188],"approach":[101,112,208],"datasets":[105,174],"called":[106,120],"DenseWeight":[107,127],"and":[108],"network":[115],"with":[117],"DenseLoss":[121,142,176,193],"based":[122],"our":[124,201],"scheme.":[126,189],"weights":[128],"points":[130,157,184],"according":[131,151],"target":[134],"value":[135],"rarities":[136],"through":[137,185,232],"kernel":[138],"density":[139],"estimation":[140],"(KDE).":[141],"adjusts":[143],"each":[144],"point\u2019s":[146],"influence":[147,159],"loss":[150],"DenseWeight,":[153],"giving":[154],"more":[158,210],"training":[162,214,238],"common":[165,228],"points.":[167,245],"We":[168],"show":[169],"multiple":[171],"differently":[172],"distributed":[173],"that":[175,200],"significantly":[177],"improves":[178],"its":[186],"density-based":[187],"Additionally,":[190],"compare":[192],"state-of-the-art":[196],"method":[197,202],"SMOGN,":[198],"finding":[199],"yields":[204],"better":[205,240],"performance.":[206],"Our":[207],"provides":[209],"control":[211],"over":[212],"as":[215],"it":[216],"enables":[217],"us":[218],"actively":[220],"decide":[221],"trade-off":[224],"between":[225],"or":[229],"cases":[231],"single":[234],"hyperparameter,":[235],"allowing":[236],"models":[241]},"counts_by_year":[{"year":2026,"cited_by_count":29},{"year":2025,"cited_by_count":59},{"year":2024,"cited_by_count":48},{"year":2023,"cited_by_count":28},{"year":2022,"cited_by_count":11}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}