{"id":"https://openalex.org/W4392911433","doi":"https://doi.org/10.3390/make6010031","title":"Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm?","display_name":"Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm?","publication_year":2024,"publication_date":"2024-03-16","ids":{"openalex":"https://openalex.org/W4392911433","doi":"https://doi.org/10.3390/make6010031"},"language":"en","primary_location":{"id":"doi:10.3390/make6010031","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/make6010031","pdf_url":"https://www.mdpi.com/2504-4990/6/1/31/pdf?version=1710757698","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"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 and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/6/1/31/pdf?version=1710757698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041142838","display_name":"Soma Onishi","orcid":"https://orcid.org/0009-0008-1633-9689"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Soma Onishi","raw_affiliation_strings":["Department Computer Science, Meiji University, Kawasaki 214-8571, Japan"],"raw_orcid":"https://orcid.org/0009-0008-1633-9689","affiliations":[{"raw_affiliation_string":"Department Computer Science, Meiji University, Kawasaki 214-8571, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049334877","display_name":"Masahiro Nishimura","orcid":"https://orcid.org/0009-0004-4575-0654"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Nishimura","raw_affiliation_strings":["Department Computer Science, Meiji University, Kawasaki 214-8571, Japan"],"raw_orcid":"https://orcid.org/0009-0004-4575-0654","affiliations":[{"raw_affiliation_string":"Department Computer Science, Meiji University, Kawasaki 214-8571, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083637960","display_name":"Ryota Fujimura","orcid":"https://orcid.org/0009-0000-4971-9444"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryota Fujimura","raw_affiliation_strings":["Department Computer Science, Meiji University, Kawasaki 214-8571, Japan"],"raw_orcid":"https://orcid.org/0009-0000-4971-9444","affiliations":[{"raw_affiliation_string":"Department Computer Science, Meiji University, Kawasaki 214-8571, Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041714982","display_name":"Yoichi Hayashi","orcid":"https://orcid.org/0000-0002-8359-374X"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yoichi Hayashi","raw_affiliation_strings":["Department Computer Science, Meiji University, Kawasaki 214-8571, Japan"],"raw_orcid":"https://orcid.org/0000-0002-8359-374X","affiliations":[{"raw_affiliation_string":"Department Computer Science, Meiji University, Kawasaki 214-8571, Japan","institution_ids":["https://openalex.org/I16656306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041142838","https://openalex.org/A5041714982"],"corresponding_institution_ids":["https://openalex.org/I16656306"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.9762,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78166352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"6","issue":"1","first_page":"658","last_page":"678"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9968000054359436,"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":0.9968000054359436,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.6424440741539001},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4976952373981476},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.43832799792289734},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.42468738555908203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3245250880718231},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24429219961166382},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.04638680815696716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6424440741539001},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4976952373981476},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.43832799792289734},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.42468738555908203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3245250880718231},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24429219961166382},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.04638680815696716}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/make6010031","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/make6010031","pdf_url":"https://www.mdpi.com/2504-4990/6/1/31/pdf?version=1710757698","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"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 and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f5a24217a0c648188b921c8db14ed8b3","is_oa":false,"landing_page_url":"https://doaj.org/article/f5a24217a0c648188b921c8db14ed8b3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"Machine Learning and Knowledge Extraction, Vol 6, Iss 1, Pp 658-678 (2024)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2504-4990/6/1/31/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/make6010031","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Machine Learning and Knowledge Extraction","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/make6010031","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/make6010031","pdf_url":"https://www.mdpi.com/2504-4990/6/1/31/pdf?version=1710757698","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"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 and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392911433.pdf","grobid_xml":"https://content.openalex.org/works/W4392911433.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W2048231652","https://openalex.org/W2101234009","https://openalex.org/W2103052024","https://openalex.org/W2111547651","https://openalex.org/W2142988211","https://openalex.org/W2155653793","https://openalex.org/W2295598076","https://openalex.org/W2342097644","https://openalex.org/W2367397349","https://openalex.org/W2473993990","https://openalex.org/W2515179890","https://openalex.org/W2624492365","https://openalex.org/W2768348081","https://openalex.org/W2789758093","https://openalex.org/W2791737674","https://openalex.org/W2887835078","https://openalex.org/W2891503716","https://openalex.org/W2911964244","https://openalex.org/W2912390193","https://openalex.org/W2945976633","https://openalex.org/W2949676527","https://openalex.org/W2963292722","https://openalex.org/W2981731882","https://openalex.org/W2991579958","https://openalex.org/W2999543921","https://openalex.org/W3001193759","https://openalex.org/W3005463189","https://openalex.org/W3014082649","https://openalex.org/W3014245704","https://openalex.org/W3049037455","https://openalex.org/W3105220378","https://openalex.org/W3121453273","https://openalex.org/W3169203486","https://openalex.org/W3187293469","https://openalex.org/W3194668998","https://openalex.org/W3201437744","https://openalex.org/W3203626883","https://openalex.org/W3211639647","https://openalex.org/W3217008232","https://openalex.org/W4200387753","https://openalex.org/W4211075414","https://openalex.org/W4212883601","https://openalex.org/W4223932288","https://openalex.org/W4225816397","https://openalex.org/W4251742697","https://openalex.org/W4292820991","https://openalex.org/W4296982335","https://openalex.org/W4309730097","https://openalex.org/W4321503229","https://openalex.org/W4391848979","https://openalex.org/W6634414075","https://openalex.org/W6675354045","https://openalex.org/W6726311770","https://openalex.org/W6757817989","https://openalex.org/W6769708748","https://openalex.org/W6833387226"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857"],"abstract_inverted_index":{"Although":[0],"machine":[1],"learning":[2],"models":[3,22],"are":[4,23],"widely":[5],"used":[6],"in":[7],"critical":[8],"domains,":[9],"their":[10,26],"complexity":[11],"and":[12,20,28,61,73,96,139,146],"poor":[13],"interpretability":[14,84,91,99,127],"remain":[15],"problematic.":[16],"Decision":[17],"trees":[18],"(DTs)":[19],"rule-based":[21],"known":[24],"for":[25,34,88],"interpretability,":[27,150,168],"numerous":[29],"studies":[30],"have":[31],"investigated":[32],"techniques":[33],"approximating":[35],"tree":[36,106],"ensembles":[37],"using":[38],"DTs":[39],"or":[40],"rule":[41,56,76,94,112],"sets,":[42],"even":[43,151],"though":[44],"these":[45,75,111],"approximators":[46],"often":[47],"overlook":[48],"interpretability.":[49],"These":[50],"methods":[51],"generate":[52],"three":[53],"types":[54],"of":[55,90,100,155,174],"sets:":[57],"DT":[58],"based,":[59],"unordered,":[60],"decision":[62],"list":[63],"based.":[64],"However,":[65],"very":[66],"few":[67],"metrics":[68],"exist":[69],"that":[70,132],"can":[71,136],"distinguish":[72],"compare":[74,110],"sets.":[77],"Therefore,":[78],"the":[79,98,101,105,115,126,153,171],"present":[80],"study":[81],"proposes":[82],"an":[83],"metric":[85],"to":[86,122],"allow":[87],"comparisons":[89],"between":[92],"different":[93],"sets":[95,113],"investigates":[97],"rules":[102,156],"generated":[103],"by":[104],"ensemble":[107],"approximators.":[108],"We":[109],"with":[114,120,134],"Recursive-Rule":[116],"eXtraction":[117],"algorithm":[118],"(Re-RX)":[119],"J48graft":[121,135],"offer":[123],"insights":[124],"into":[125],"gap.":[128],"The":[129],"results":[130,166],"indicate":[131],"Re-RX":[133],"handle":[137],"categorical":[138],"numerical":[140],"attributes":[141],"separately,":[142],"has":[143],"simple":[144],"rules,":[145],"achieves":[147],"a":[148,160],"high":[149],"when":[152],"number":[154,173],"is":[157],"large.":[158],"RuleCOSI+,":[159],"state-of-the-art":[161],"method,":[162],"showed":[163],"significantly":[164],"lower":[165],"regarding":[167],"but":[169],"had":[170],"smallest":[172],"rules.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}