{"id":"https://openalex.org/W4390101835","doi":"https://doi.org/10.1007/s10994-023-06488-6","title":"logicDT: a procedure for identifying response-associated interactions between binary predictors","display_name":"logicDT: a procedure for identifying response-associated interactions between binary predictors","publication_year":2023,"publication_date":"2023-12-22","ids":{"openalex":"https://openalex.org/W4390101835","doi":"https://doi.org/10.1007/s10994-023-06488-6"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-023-06488-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06488-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06488-6.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/s10994-023-06488-6.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052722892","display_name":"Michael Lau","orcid":"https://orcid.org/0000-0002-5327-8351"},"institutions":[{"id":"https://openalex.org/I4210101048","display_name":"Leibniz Institute of Environmental Medicine","ror":"https://ror.org/0163xqp73","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210101048"]},{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Lau","raw_affiliation_strings":["IUF - Leibniz Research Institute for Environmental Medicine, Auf\u2019m Hennekamp 50, 40225, D\u00fcsseldorf, Germany","Mathematical Institute, Heinrich Heine University, Universit\u00e4tsstrasse 1, 40225, D\u00fcsseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"IUF - Leibniz Research Institute for Environmental Medicine, Auf\u2019m Hennekamp 50, 40225, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I4210101048"]},{"raw_affiliation_string":"Mathematical Institute, Heinrich Heine University, Universit\u00e4tsstrasse 1, 40225, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036569172","display_name":"Tamara Schikowski","orcid":"https://orcid.org/0000-0002-4559-9374"},"institutions":[{"id":"https://openalex.org/I4210101048","display_name":"Leibniz Institute of Environmental Medicine","ror":"https://ror.org/0163xqp73","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210101048"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tamara Schikowski","raw_affiliation_strings":["IUF - Leibniz Research Institute for Environmental Medicine, Auf\u2019m Hennekamp 50, 40225, D\u00fcsseldorf, Germany","IUF - Leibniz Research Institute for Environmental Medicine, Auf'm Hennekamp 50, 40225, D\u00fcsseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"IUF - Leibniz Research Institute for Environmental Medicine, Auf\u2019m Hennekamp 50, 40225, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I4210101048"]},{"raw_affiliation_string":"IUF - Leibniz Research Institute for Environmental Medicine, Auf'm Hennekamp 50, 40225, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I4210101048"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037974033","display_name":"Holger Schwender","orcid":"https://orcid.org/0000-0002-2858-6715"},"institutions":[{"id":"https://openalex.org/I44260953","display_name":"Heinrich Heine University D\u00fcsseldorf","ror":"https://ror.org/024z2rq82","country_code":"DE","type":"education","lineage":["https://openalex.org/I44260953"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Holger Schwender","raw_affiliation_strings":["Mathematical Institute, Heinrich Heine University, Universit\u00e4tsstrasse 1, 40225, D\u00fcsseldorf, Germany"],"affiliations":[{"raw_affiliation_string":"Mathematical Institute, Heinrich Heine University, Universit\u00e4tsstrasse 1, 40225, D\u00fcsseldorf, Germany","institution_ids":["https://openalex.org/I44260953"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052722892"],"corresponding_institution_ids":["https://openalex.org/I4210101048","https://openalex.org/I44260953"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.3439,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67949508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"113","issue":"2","first_page":"933","last_page":"992"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9847999811172485,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9847999811172485,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.974399983882904,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9667999744415283,"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/interpretability","display_name":"Interpretability","score":0.9138379693031311},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6433706283569336},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6264669895172119},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.625450849533081},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5976895093917847},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5641599297523499},{"id":"https://openalex.org/keywords/binary-decision-diagram","display_name":"Binary decision diagram","score":0.499096155166626},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4429870843887329},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4220025837421417},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37418821454048157},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3318691551685333},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28950467705726624},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10870012640953064}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9138379693031311},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6433706283569336},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6264669895172119},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.625450849533081},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5976895093917847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5641599297523499},{"id":"https://openalex.org/C3309909","wikidata":"https://www.wikidata.org/wiki/Q864155","display_name":"Binary decision diagram","level":2,"score":0.499096155166626},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4429870843887329},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4220025837421417},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37418821454048157},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3318691551685333},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28950467705726624},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10870012640953064},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10994-023-06488-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06488-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06488-6.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:docserv.uni-duesseldorf.de:68937","is_oa":false,"landing_page_url":"https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=68937","pdf_url":null,"source":{"id":"https://openalex.org/S4406922359","display_name":"Univ. Duesseldorf: Duesseldorfer Dokumenten- und Publikationsserver","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Artikel, Aufsatz"}],"best_oa_location":{"id":"doi:10.1007/s10994-023-06488-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-023-06488-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-023-06488-6.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":0.7300000190734863,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G2493638443","display_name":null,"funder_award_id":"427806116","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G2623008088","display_name":null,"funder_award_id":"617.0-FP266","funder_id":"https://openalex.org/F4320321900","funder_display_name":"Deutsche Gesetzliche Unfallversicherung"},{"id":"https://openalex.org/G3494183208","display_name":null,"funder_award_id":"RTG 2624","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5106512922","display_name":null,"funder_award_id":"Deutsche Forschungsgemeinschaft (DFG","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6743243744","display_name":null,"funder_award_id":"unknown","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G762232396","display_name":null,"funder_award_id":"Project","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8330397625","display_name":null,"funder_award_id":"Funding","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8928656450","display_name":null,"funder_award_id":"Projekt DEAL","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320321900","display_name":"Deutsche Gesetzliche Unfallversicherung","ror":"https://ror.org/01agh0847"},{"id":"https://openalex.org/F4320321951","display_name":"Heinrich-Heine-Universit\u00e4t D\u00fcsseldorf","ror":"https://ror.org/024z2rq82"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4390101835.pdf"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W13188192","https://openalex.org/W129209152","https://openalex.org/W840198283","https://openalex.org/W1484867920","https://openalex.org/W1509515766","https://openalex.org/W1593394816","https://openalex.org/W1678356000","https://openalex.org/W1712584899","https://openalex.org/W2002267074","https://openalex.org/W2003275871","https://openalex.org/W2013375584","https://openalex.org/W2019841176","https://openalex.org/W2024060531","https://openalex.org/W2033072307","https://openalex.org/W2036931828","https://openalex.org/W2045803758","https://openalex.org/W2048231652","https://openalex.org/W2051177122","https://openalex.org/W2059958079","https://openalex.org/W2079995796","https://openalex.org/W2086099578","https://openalex.org/W2087200541","https://openalex.org/W2099830529","https://openalex.org/W2100674457","https://openalex.org/W2110065044","https://openalex.org/W2123427204","https://openalex.org/W2125055259","https://openalex.org/W2133298971","https://openalex.org/W2135046866","https://openalex.org/W2141007997","https://openalex.org/W2143865774","https://openalex.org/W2148603752","https://openalex.org/W2149421006","https://openalex.org/W2155279768","https://openalex.org/W2157395790","https://openalex.org/W2161633633","https://openalex.org/W2162387923","https://openalex.org/W2207674461","https://openalex.org/W2295598076","https://openalex.org/W2322754609","https://openalex.org/W2330820318","https://openalex.org/W2507263886","https://openalex.org/W2582743722","https://openalex.org/W2584238682","https://openalex.org/W2604736517","https://openalex.org/W2768650472","https://openalex.org/W2787894218","https://openalex.org/W2898748204","https://openalex.org/W2904939492","https://openalex.org/W2911964244","https://openalex.org/W2922844241","https://openalex.org/W2951539267","https://openalex.org/W2997018541","https://openalex.org/W2999615587","https://openalex.org/W3022244144","https://openalex.org/W3037729706","https://openalex.org/W3041731231","https://openalex.org/W3098524006","https://openalex.org/W3098689300","https://openalex.org/W3102027041","https://openalex.org/W3104060823","https://openalex.org/W3138703676","https://openalex.org/W3195279903","https://openalex.org/W3208089818","https://openalex.org/W4212883601","https://openalex.org/W4220673804","https://openalex.org/W4225863264","https://openalex.org/W4226317941","https://openalex.org/W4230674625","https://openalex.org/W4231211138","https://openalex.org/W4302422232","https://openalex.org/W4399589631","https://openalex.org/W6676179485","https://openalex.org/W6679060694","https://openalex.org/W6768708636","https://openalex.org/W6843735874","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4361193272","https://openalex.org/W4361806667","https://openalex.org/W4293151273","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4313289487","https://openalex.org/W4321636153","https://openalex.org/W3011239835"],"abstract_inverted_index":{"Abstract":[0],"Interactions":[1],"between":[2,80,171],"predictors":[3],"play":[4],"an":[5],"important":[6],"role":[7],"in":[8,59,92,185],"many":[9],"applications.":[10,190],"Popular":[11],"and":[12,53,83,117,145,169,187],"successful":[13],"tree-based":[14],"supervised":[15],"learning":[16,183],"methods":[17],"such":[18,46],"as":[19,47,139],"random":[20,63],"forests":[21,64],"or":[22,65],"logic":[23,96,105],"regression":[24],"can":[25],"incorporate":[26,70],"interactions":[27,71,91,170],"associated":[28],"with":[29,55,72],"the":[30,60,90,93,121,148,161],"considered":[31],"outcome":[32],"without":[33],"specifying":[34],"which":[35],"variables":[36,141,168],"might":[37],"interact.":[38],"Nonetheless,":[39],"these":[40,137,192],"algorithms":[41,184],"suffer":[42],"from":[43],"certain":[44],"drawbacks":[45,122],"limited":[48],"interpretability":[49],"of":[50,62,95,123,130,133,163],"model":[51],"predictions":[52],"difficulties":[54],"negligible":[56],"marginal":[57],"effects":[58],"case":[61,94],"not":[66,84],"being":[67,75],"able":[68,197],"to":[69,77,113,119,179,198],"continuous":[73],"variables,":[74],"restricted":[76],"additive":[78],"structures":[79],"Boolean":[81,134],"terms,":[82,165],"directly":[85],"considering":[86,131],"conjunctions":[87],"that":[88,109],"reveal":[89],"regression.":[97],"We,":[98],"therefore,":[99],"propose":[100],"a":[101,157],"novel":[102],"method":[103,176],"called":[104],"decision":[106,143],"trees":[107],"(logicDT)":[108],"is":[110,153,177,196],"specifically":[111],"tailored":[112],"binary":[114],"input":[115,140,167,172],"data":[116,189],"helps":[118],"overcome":[120],"existing":[124],"methods.":[125],"The":[126],"main":[127],"idea":[128],"consists":[129],"sets":[132],"conjunctions,":[135],"using":[136],"terms":[138],"for":[142,147,159],"trees,":[144],"searching":[146],"best":[149],"performing":[150],"model.":[151],"logicDT":[152,195],"also":[154],"accompanied":[155],"by":[156],"framework":[158],"estimating":[160],"importance":[162],"identified":[164],"i.e.,":[166],"variables.":[173],"This":[174],"new":[175],"compared":[178],"other":[180],"popular":[181],"statistical":[182],"simulations":[186],"real":[188],"As":[191],"evaluations":[193],"show,":[194],"yield":[199],"high":[200],"prediction":[201],"performances":[202],"while":[203],"maintaining":[204],"interpretability.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}