{"id":"https://openalex.org/W4389438972","doi":"https://doi.org/10.1145/3636512","title":"Exploring the Learning Difficulty of Data: Theory and Measure","display_name":"Exploring the Learning Difficulty of Data: Theory and Measure","publication_year":2023,"publication_date":"2023-12-07","ids":{"openalex":"https://openalex.org/W4389438972","doi":"https://doi.org/10.1145/3636512"},"language":"en","primary_location":{"id":"doi:10.1145/3636512","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3636512","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101454449","display_name":"Weiyao Zhu","orcid":"https://orcid.org/0000-0001-7610-295X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiyao Zhu","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, China"],"raw_orcid":"https://orcid.org/0000-0001-7610-295X","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046034610","display_name":"Ou Wu","orcid":"https://orcid.org/0000-0002-6106-6914"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ou Wu","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, China"],"raw_orcid":"https://orcid.org/0000-0002-6106-6914","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048240336","display_name":"Fengguang Su","orcid":"https://orcid.org/0000-0002-3824-8306"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengguang Su","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, China"],"raw_orcid":"https://orcid.org/0000-0002-3824-8306","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059842534","display_name":"Yingjun Deng","orcid":"https://orcid.org/0000-0001-8467-7526"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingjun Deng","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, China"],"raw_orcid":"https://orcid.org/0000-0001-8467-7526","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.7931,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.77398983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"18","issue":"4","first_page":"1","last_page":"37"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9988999962806702,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.996399998664856,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/weighting","display_name":"Weighting","score":0.6876732110977173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6808377504348755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6773422956466675},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6611665487289429},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6597771048545837},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.611330509185791},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5867714285850525},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5614089965820312},{"id":"https://openalex.org/keywords/algorithmic-learning-theory","display_name":"Algorithmic learning theory","score":0.5479774475097656},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5272982120513916},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4516911506652832},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.396731972694397},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24067071080207825},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.23463022708892822},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.1309390664100647}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6876732110977173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6808377504348755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6773422956466675},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6611665487289429},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6597771048545837},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.611330509185791},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5867714285850525},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5614089965820312},{"id":"https://openalex.org/C32254414","wikidata":"https://www.wikidata.org/wiki/Q4724364","display_name":"Algorithmic learning theory","level":3,"score":0.5479774475097656},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5272982120513916},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4516911506652832},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.396731972694397},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24067071080207825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.23463022708892822},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.1309390664100647},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3636512","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3636512","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G4406340811","display_name":null,"funder_award_id":"62076178","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":66,"referenced_works":["https://openalex.org/W591148856","https://openalex.org/W1480376833","https://openalex.org/W1638203394","https://openalex.org/W1921293667","https://openalex.org/W1988790447","https://openalex.org/W2076118331","https://openalex.org/W2078807908","https://openalex.org/W2094051685","https://openalex.org/W2096733369","https://openalex.org/W2102098892","https://openalex.org/W2144978720","https://openalex.org/W2170181617","https://openalex.org/W2185967890","https://openalex.org/W2194775991","https://openalex.org/W2312481402","https://openalex.org/W2600383743","https://openalex.org/W2618530766","https://openalex.org/W2682189153","https://openalex.org/W2767106145","https://openalex.org/W2803237185","https://openalex.org/W2803921058","https://openalex.org/W2885593519","https://openalex.org/W2893061644","https://openalex.org/W2901278409","https://openalex.org/W2901499219","https://openalex.org/W2903996579","https://openalex.org/W2911663172","https://openalex.org/W2911964244","https://openalex.org/W2948210185","https://openalex.org/W2949416664","https://openalex.org/W2950352605","https://openalex.org/W2952967169","https://openalex.org/W2963351448","https://openalex.org/W2971899271","https://openalex.org/W2988966271","https://openalex.org/W3008906732","https://openalex.org/W3009888028","https://openalex.org/W3012245929","https://openalex.org/W3033630125","https://openalex.org/W3034403194","https://openalex.org/W3043235637","https://openalex.org/W3048627609","https://openalex.org/W3080532707","https://openalex.org/W3086111326","https://openalex.org/W3097153076","https://openalex.org/W3105402527","https://openalex.org/W3115775989","https://openalex.org/W3116172555","https://openalex.org/W3118565077","https://openalex.org/W3119113126","https://openalex.org/W3133456353","https://openalex.org/W3134390489","https://openalex.org/W3163842339","https://openalex.org/W3164942072","https://openalex.org/W3175939672","https://openalex.org/W3210087465","https://openalex.org/W4220754565","https://openalex.org/W4234817746","https://openalex.org/W4239510810","https://openalex.org/W4287022692","https://openalex.org/W4287659519","https://openalex.org/W4288365786","https://openalex.org/W4299286820","https://openalex.org/W4300531725","https://openalex.org/W4389252620","https://openalex.org/W6966490198"],"related_works":["https://openalex.org/W3212699736","https://openalex.org/W3105492668","https://openalex.org/W4234125632","https://openalex.org/W1991515142","https://openalex.org/W2913506004","https://openalex.org/W2050153923","https://openalex.org/W2774684800","https://openalex.org/W2898649268","https://openalex.org/W4289752279","https://openalex.org/W1982773423"],"abstract_inverted_index":{"\u2018\u2018Easy/hard":[0],"sample\u201d":[1],"is":[2,19,59,134,149],"a":[3,17,21,46,74,83,128],"popular":[4],"parlance":[5],"in":[6,34,64,214],"machine":[7,35,117],"learning.":[8],"Learning":[9],"difficulty":[10,30,50,58,89,97,111,133,148],"of":[11,27,48,90,116,131,139,146,154,167,179],"samples":[12],"refers":[13],"to":[14,61,81],"how":[15],"easy/hard":[16],"sample":[18],"during":[20],"learning":[22,29,36,40,49,57,88,96,110,132,147],"procedure.":[23],"An":[24],"increasing":[25],"need":[26],"measuring":[28],"demonstrates":[31],"its":[32],"importance":[33],"(e.g.,":[37],"difficulty-based":[38],"weighting":[39,182,198],"strategies).":[41],"Previous":[42],"literature":[43],"has":[44],"proposed":[45,135,150,206,220],"number":[47],"measures.":[51],"However,":[52],"no":[53],"comprehensive":[54],"investigation":[55],"for":[56,87,95],"available":[60],"date,":[62],"resulting":[63],"that":[65,218],"nearly":[66],"all":[67,189],"existing":[68],"measures":[69,226],"are":[70,98,125,174],"heuristically":[71],"defined":[72],"without":[73],"rigorous":[75],"theoretical":[76,85,129,156,168,207],"foundation.":[77],"This":[78],"study":[79,86],"attempts":[80],"conduct":[82],"pilot":[84],"samples.":[91],"First,":[92],"influential":[93,106,194],"factors":[94],"summarized.":[99],"Under":[100],"various":[101],"situations":[102,190],"conducted":[103,191],"by":[104,158,177,192],"summarized":[105,193],"factors,":[107],"correlations":[108],"between":[109],"and":[112,122,170,184,209],"two":[113,141],"vital":[114],"criteria":[115],"learning,":[118],"namely,":[119],"generalization":[120],"error":[121],"model":[123],"complexity,":[124],"revealed.":[126],"Second,":[127],"definition":[130,157,169,208],"on":[136],"the":[137,152,155,160,165,171,205,219,224,228],"basis":[138],"these":[140,215],"criteria.":[142],"A":[143],"practical":[144,172],"measure":[145,173,221],"under":[151,188,204],"direction":[153],"importing":[159],"bias-variance":[161],"trade-off":[162],"theory.":[163],"Subsequently,":[164],"rationality":[166],"demonstrated,":[175],"respectively,":[176],"analysis":[178],"several":[180],"classical":[181],"methods":[183,199],"abundant":[185],"experiments":[186,216],"realized":[187],"factors.":[195],"The":[196,212],"mentioned":[197],"can":[200],"be":[201],"reasonably":[202],"explained":[203],"concerned":[210],"propositions.":[211],"comparison":[213],"indicates":[217],"significantly":[222],"outperforms":[223],"other":[225],"throughout":[227],"experiments.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}