{"id":"https://openalex.org/W4401214914","doi":"https://doi.org/10.1145/3638530.3664129","title":"Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks","display_name":"Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks","publication_year":2024,"publication_date":"2024-07-14","ids":{"openalex":"https://openalex.org/W4401214914","doi":"https://doi.org/10.1145/3638530.3664129"},"language":"en","primary_location":{"id":"doi:10.1145/3638530.3664129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3638530.3664129","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3638530.3664129","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045149972","display_name":"Mohammed Ghaith Altarabichi","orcid":"https://orcid.org/0000-0002-6040-2269"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Mohammed Ghaith Altarabichi","raw_affiliation_strings":["Halmstad University, G\u00f6teborg, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, G\u00f6teborg, Sweden","institution_ids":["https://openalex.org/I746986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009075178","display_name":"Abdallah Alabdallah","orcid":"https://orcid.org/0000-0001-9416-5647"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Abdallah Alabdallah","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065509988","display_name":"Sepideh Pashami","orcid":"https://orcid.org/0000-0003-3272-4145"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Sepideh Pashami","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019978147","display_name":"Thorsteinn R\u00f6gnvaldsson","orcid":"https://orcid.org/0000-0001-5163-2997"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Thorsteinn R\u00f6gnvaldsson","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032811876","display_name":"S\u0142awomir Nowaczyk","orcid":"https://orcid.org/0000-0002-7796-5201"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"S\u0142awomir Nowaczyk","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051924946","display_name":"Mattias Ohlsson","orcid":"https://orcid.org/0000-0003-1145-4297"},"institutions":[{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Mattias Ohlsson","raw_affiliation_strings":["Halmstad University, Halmstad, Sweden"],"affiliations":[{"raw_affiliation_string":"Halmstad University, Halmstad, Sweden","institution_ids":["https://openalex.org/I746986"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5045149972"],"corresponding_institution_ids":["https://openalex.org/I746986"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1256572,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1863","last_page":"1869"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9908000230789185,"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":0.9908000230789185,"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/T10320","display_name":"Neural Networks and Applications","score":0.9861000180244446,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/concordance","display_name":"Concordance","score":0.7815306186676025},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6567524671554565},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6566580533981323},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5775728225708008},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5689208507537842},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5616931319236755},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5004196166992188},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4962671399116516},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4870041012763977},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.4194491505622864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40510255098342896},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2526209056377411},{"id":"https://openalex.org/keywords/evolutionary-biology","display_name":"Evolutionary biology","score":0.22243249416351318},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.22083088755607605},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.15138274431228638}],"concepts":[{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.7815306186676025},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6567524671554565},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6566580533981323},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5775728225708008},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5689208507537842},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5616931319236755},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5004196166992188},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4962671399116516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4870041012763977},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.4194491505622864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40510255098342896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2526209056377411},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.22243249416351318},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.22083088755607605},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.15138274431228638},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3638530.3664129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3638530.3664129","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3638530.3664129","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3638530.3664129","pdf_url":null,"source":{"id":"https://openalex.org/S4363608771","display_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320307330","display_name":"Knowledge Foundation","ror":"https://ror.org/00v64cg28"},{"id":"https://openalex.org/F4320321030","display_name":"VINNOVA","ror":"https://ror.org/01kd5m353"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1968657129","https://openalex.org/W2084139018","https://openalex.org/W2139255721","https://openalex.org/W2163042952","https://openalex.org/W2235175863","https://openalex.org/W2593744649","https://openalex.org/W2789172526","https://openalex.org/W2950852589","https://openalex.org/W2954512644","https://openalex.org/W2963246719","https://openalex.org/W2963678809","https://openalex.org/W3101072153","https://openalex.org/W3123284220","https://openalex.org/W3134068016","https://openalex.org/W3147894994","https://openalex.org/W3174427901","https://openalex.org/W3192464041","https://openalex.org/W3210442909","https://openalex.org/W4292242240","https://openalex.org/W4302226579","https://openalex.org/W4306177946","https://openalex.org/W4319586224","https://openalex.org/W4376545565","https://openalex.org/W4383960481","https://openalex.org/W4384024444","https://openalex.org/W4391074546","https://openalex.org/W4391942283","https://openalex.org/W4393029506","https://openalex.org/W6750869745","https://openalex.org/W6764798969"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W1969346022","https://openalex.org/W2034959125","https://openalex.org/W2355687852"],"abstract_inverted_index":{"In":[0],"this":[1,107],"work,":[2],"we":[3,33,71,109],"use":[4],"an":[5,35],"Evolutionary":[6],"Algorithm":[7],"(EA)":[8],"to":[9,98,143],"discover":[10],"a":[11,42,73,93,112],"novel":[12,113],"Neural":[13],"Network":[14],"(NN)":[15],"regression-based":[16,44],"survival":[17,114],"loss":[18,45,56,91,116],"function":[19,46,117],"with":[20],"the":[21,25,49,67,78,81,85,90,128],"aim":[22],"of":[23,66,80,89],"improving":[24,102],"C-index":[26],"performance.":[27],"Our":[28],"contribution":[29],"is":[30,96],"threefold;":[31],"firstly,":[32],"propose":[34,110],"evolutionary":[36,68],"meta-learning":[37],"algorithm":[38,52],"SAGAloss":[39],"for":[40,84,132],"optimizing":[41],"neural-network":[43],"that":[47,58,76,95,118],"maximizes":[48],"C-index;":[50],"our":[51,64,145],"consistently":[53],"discovers":[54],"specialized":[55],"functions":[57],"outperform":[59],"MSCE.":[60],"Secondly,":[61],"based":[62,105],"on":[63,106,139],"analysis":[65],"search":[69],"results,":[70],"highlight":[72],"non-intuitive":[74],"insight":[75],"signifies":[77],"importance":[79],"non-zero":[82],"gradient":[83],"censored":[86,133],"cases":[87],"part":[88],"function,":[92],"property":[94],"shown":[97],"be":[99,120],"useful":[100],"in":[101],"concordance.":[103],"Finally,":[104],"insight,":[108],"MSCESp,":[111],"regression":[115],"can":[119],"used":[121],"off-the-shelf":[122],"and":[123],"generally":[124],"performs":[125],"better":[126],"than":[127],"Mean":[129],"Squared":[130],"Error":[131],"cases.":[134],"We":[135],"performed":[136],"extensive":[137],"experiments":[138],"19":[140],"benchmark":[141],"datasets":[142],"validate":[144],"findings.":[146]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}