{"id":"https://openalex.org/W4210721736","doi":"https://doi.org/10.1080/01969722.2022.2030007","title":"Improving the Prediction of Project Success in the Telecom Sector by Means of Advanced Data Balancing","display_name":"Improving the Prediction of Project Success in the Telecom Sector by Means of Advanced Data Balancing","publication_year":2022,"publication_date":"2022-02-01","ids":{"openalex":"https://openalex.org/W4210721736","doi":"https://doi.org/10.1080/01969722.2022.2030007"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2022.2030007","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2030007","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","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/A5007869197","display_name":"Nu\u00f1o Basurto","orcid":"https://orcid.org/0000-0001-7289-4689"},"institutions":[{"id":"https://openalex.org/I46176106","display_name":"Universidad de Burgos","ror":"https://ror.org/049da5t36","country_code":"ES","type":"education","lineage":["https://openalex.org/I46176106"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Nu\u00f1o Basurto","raw_affiliation_strings":["Departamento de Ingenier\u00eda Inform\u00e1tica, Grupo de Inteligencia Computacional Aplicada (GICAP), Universidad de Burgos, Burgos, Spain"],"affiliations":[{"raw_affiliation_string":"Departamento de Ingenier\u00eda Inform\u00e1tica, Grupo de Inteligencia Computacional Aplicada (GICAP), Universidad de Burgos, Burgos, Spain","institution_ids":["https://openalex.org/I46176106"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048904021","display_name":"Alfredo Jim\u00e9nez","orcid":"https://orcid.org/0000-0001-7811-5113"},"institutions":[{"id":"https://openalex.org/I51205905","display_name":"Kedge Business School","ror":"https://ror.org/00wk3s644","country_code":"FR","type":"education","lineage":["https://openalex.org/I51205905"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Alfredo Jim\u00e9nez","raw_affiliation_strings":["Department of Management, KEDGE Business School, Bordeaux, France"],"affiliations":[{"raw_affiliation_string":"Department of Management, KEDGE Business School, Bordeaux, France","institution_ids":["https://openalex.org/I51205905"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046068853","display_name":"Se\u00e7il Bayraktar","orcid":"https://orcid.org/0000-0002-5669-4465"},"institutions":[{"id":"https://openalex.org/I129162050","display_name":"TBS Education","ror":"https://ror.org/0349y2q65","country_code":"FR","type":"education","lineage":["https://openalex.org/I129162050","https://openalex.org/I4405258862"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Secil Bayraktar","raw_affiliation_strings":["Department of Human Resources Management and Business Law, TBS Business School, Toulouse, France"],"affiliations":[{"raw_affiliation_string":"Department of Human Resources Management and Business Law, TBS Business School, Toulouse, France","institution_ids":["https://openalex.org/I129162050"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075173310","display_name":"\u00c1lvaro Herrero","orcid":"https://orcid.org/0000-0002-2444-5384"},"institutions":[{"id":"https://openalex.org/I46176106","display_name":"Universidad de Burgos","ror":"https://ror.org/049da5t36","country_code":"ES","type":"education","lineage":["https://openalex.org/I46176106"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"\u00c1lvaro Herrero","raw_affiliation_strings":["Departamento de Ingenier\u00eda Inform\u00e1tica, Grupo de Inteligencia Computacional Aplicada (GICAP), Universidad de Burgos, Burgos, Spain"],"affiliations":[{"raw_affiliation_string":"Departamento de Ingenier\u00eda Inform\u00e1tica, Grupo de Inteligencia Computacional Aplicada (GICAP), Universidad de Burgos, Burgos, Spain","institution_ids":["https://openalex.org/I46176106"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5007869197"],"corresponding_institution_ids":["https://openalex.org/I46176106"],"apc_list":null,"apc_paid":null,"fwci":0.5305,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69264745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"54","issue":"3","first_page":"306","last_page":"320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.991599977016449,"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":0.991599977016449,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.8918265104293823},{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.8696792721748352},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7544020414352417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6742057204246521},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5873059630393982},{"id":"https://openalex.org/keywords/investment","display_name":"Investment (military)","score":0.4512646198272705},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.42186301946640015},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39453285932540894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38928258419036865},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.11132559180259705}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8918265104293823},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.8696792721748352},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7544020414352417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6742057204246521},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5873059630393982},{"id":"https://openalex.org/C27548731","wikidata":"https://www.wikidata.org/wiki/Q88272","display_name":"Investment (military)","level":3,"score":0.4512646198272705},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.42186301946640015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39453285932540894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38928258419036865},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.11132559180259705},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2022.2030007","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2022.2030007","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1971402166","https://openalex.org/W1993333947","https://openalex.org/W1996523702","https://openalex.org/W2006825483","https://openalex.org/W2026197177","https://openalex.org/W2028383926","https://openalex.org/W2075028294","https://openalex.org/W2078390071","https://openalex.org/W2082976842","https://openalex.org/W2083174054","https://openalex.org/W2085831731","https://openalex.org/W2087347434","https://openalex.org/W2119821739","https://openalex.org/W2125283600","https://openalex.org/W2125889350","https://openalex.org/W2132791018","https://openalex.org/W2148143831","https://openalex.org/W2163159297","https://openalex.org/W2544836728","https://openalex.org/W2594855749","https://openalex.org/W2895455299","https://openalex.org/W2910532291","https://openalex.org/W2910887233","https://openalex.org/W2911964244","https://openalex.org/W2941736216","https://openalex.org/W2947028260","https://openalex.org/W2982284512","https://openalex.org/W3015461837","https://openalex.org/W3045622727","https://openalex.org/W3081737282","https://openalex.org/W3085449685","https://openalex.org/W3097632394","https://openalex.org/W3109522940","https://openalex.org/W3152783047","https://openalex.org/W3163489340","https://openalex.org/W4230253934"],"related_works":["https://openalex.org/W4308469503","https://openalex.org/W32988189","https://openalex.org/W2904737874","https://openalex.org/W4389233021","https://openalex.org/W2399571531","https://openalex.org/W80466363","https://openalex.org/W2947132063","https://openalex.org/W4288337828","https://openalex.org/W4287816717","https://openalex.org/W4390415670"],"abstract_inverted_index":{"As":[0],"governments":[1],"access":[2],"capital,":[3],"technology,":[4],"and":[5,63,95,113,154,168],"managerial":[6],"expertise":[7],"from":[8,124],"private":[9,49],"investors,":[10],"there":[11],"is":[12,81,97],"an":[13,44],"increasing":[14],"trend":[15],"of":[16,26,33,60,70,74,77,82,87,141,178],"privatizations":[17],"in":[18,30,68,79,99],"infrastructure":[19],"projects":[20],"worldwide.":[21],"Given":[22,53],"the":[23,31,54,58,61,72,75,125,139,175,181],"large":[24,65],"size":[25],"these":[27],"investments,":[28],"notably":[29],"sector":[32],"telecommunications,":[34],"investors":[35],"typically":[36],"create":[37],"a":[38],"consortium":[39],"with":[40,161],"other":[41,163],"interested":[42],"firms,":[43],"investment":[45],"vehicle":[46],"known":[47],"as":[48],"participation":[50],"project":[51],"(PPP).":[52],"critical":[55],"repercussions":[56],"on":[57,130,180],"rest":[59],"economy,":[62],"its":[64],"financial":[66],"losses":[67],"case":[69],"failure,":[71],"prediction":[73],"success":[76,86],"PPPs":[78,88,120],"telecommunications":[80],"utmost":[83],"importance.":[84],"The":[85,128,171],"can":[89],"be":[90],"predicted":[91],"by":[92,138,184],"Machine":[93],"Learning":[94],"it":[96],"probed":[98],"this":[100,131],"article.":[101],"Hence,":[102],"widely":[103],"acknowledged":[104],"classifiers":[105,179],"(k-nearest":[106],"neighbors":[107],"[k-NNs],":[108],"support":[109],"vector":[110],"machines":[111],"[SVMs],":[112],"random":[114,152],"forest":[115],"[RF])":[116],"are":[117,135],"applied":[118],"to":[119],"publicly":[121],"available":[122],"data":[123,142],"World":[126],"Bank.":[127],"results":[129,173],"highly":[132],"imbalanced":[133],"dataset":[134,182],"greatly":[136],"improved":[137,183],"application":[140,177],"balancing":[143],"techniques.":[144,186],"It":[145],"includes":[146],"some":[147,162],"standard":[148],"ones":[149,165],"(random":[150],"oversampling,":[151],"undersampling,":[153],"synthetic":[155],"minority":[156],"oversampling":[157],"technique":[158],"[SMOTE]),":[159],"together":[160],"advanced":[164],"(density-based":[166],"SMOTE":[167],"borderline":[169],"SMOTE).":[170],"satisfactory":[172],"validate":[174],"proposed":[176],"data-balancing":[185]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
