{"id":"https://openalex.org/W3041741730","doi":"https://doi.org/10.1145/3404663.3404681","title":"Assessing Employee Attrition Using Classifications Algorithms","display_name":"Assessing Employee Attrition Using Classifications Algorithms","publication_year":2020,"publication_date":"2020-05-15","ids":{"openalex":"https://openalex.org/W3041741730","doi":"https://doi.org/10.1145/3404663.3404681","mag":"3041741730"},"language":"en","primary_location":{"id":"doi:10.1145/3404663.3404681","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404663.3404681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Information System and Data Mining","raw_type":"proceedings-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/A5012799320","display_name":"Fatma Ozdemir","orcid":null},"institutions":[{"id":"https://openalex.org/I95293974","display_name":"Abdullah G\u00fcl University","ror":"https://ror.org/00zdyy359","country_code":"TR","type":"education","lineage":["https://openalex.org/I95293974"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Fatma Ozdemir","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey","institution_ids":["https://openalex.org/I95293974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087032049","display_name":"Mustafa Co\u015fkun","orcid":"https://orcid.org/0000-0003-4805-1416"},"institutions":[{"id":"https://openalex.org/I95293974","display_name":"Abdullah G\u00fcl University","ror":"https://ror.org/00zdyy359","country_code":"TR","type":"education","lineage":["https://openalex.org/I95293974"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Mustafa Coskun","raw_affiliation_strings":["Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey","institution_ids":["https://openalex.org/I95293974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040023822","display_name":"Cengiz Gezer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cengiz Gezer","raw_affiliation_strings":["Research &amp; Development Center, Adesso Turkey, Istanbul, Turkey"],"affiliations":[{"raw_affiliation_string":"Research &amp; Development Center, Adesso Turkey, Istanbul, Turkey","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078903096","display_name":"Vehbi \u00c7a\u011fr\u0131 G\u00fcng\u00f6r","orcid":"https://orcid.org/0000-0003-0803-8372"},"institutions":[{"id":"https://openalex.org/I95293974","display_name":"Abdullah G\u00fcl University","ror":"https://ror.org/00zdyy359","country_code":"TR","type":"education","lineage":["https://openalex.org/I95293974"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"V. Cagri Gungor","raw_affiliation_strings":["Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey","institution_ids":["https://openalex.org/I95293974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5012799320"],"corresponding_institution_ids":["https://openalex.org/I95293974"],"apc_list":null,"apc_paid":null,"fwci":2.3133,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.88792888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12415","display_name":"Employer Branding and e-HRM","score":0.9053999781608582,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/attrition","display_name":"Attrition","score":0.8419519662857056},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6788236498832703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6594303846359253},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6470134258270264},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6347553730010986},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5903435945510864},{"id":"https://openalex.org/keywords/c4.5-algorithm","display_name":"C4.5 algorithm","score":0.5791019797325134},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5491164922714233},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.518397867679596},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4698072075843811},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4673044681549072},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.43081021308898926},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.42340725660324097},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4183039963245392},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3982505798339844},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17754989862442017}],"concepts":[{"id":"https://openalex.org/C2780553607","wikidata":"https://www.wikidata.org/wiki/Q684868","display_name":"Attrition","level":2,"score":0.8419519662857056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6788236498832703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6594303846359253},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6470134258270264},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6347553730010986},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5903435945510864},{"id":"https://openalex.org/C52003472","wikidata":"https://www.wikidata.org/wiki/Q1022655","display_name":"C4.5 algorithm","level":4,"score":0.5791019797325134},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5491164922714233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.518397867679596},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4698072075843811},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4673044681549072},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.43081021308898926},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.42340725660324097},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4183039963245392},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3982505798339844},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17754989862442017},{"id":"https://openalex.org/C199343813","wikidata":"https://www.wikidata.org/wiki/Q12128","display_name":"Dentistry","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3404663.3404681","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3404663.3404681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Information System and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W155161819","https://openalex.org/W1803168886","https://openalex.org/W2140190241","https://openalex.org/W2483451959","https://openalex.org/W2769002462","https://openalex.org/W2801615787","https://openalex.org/W2897358488","https://openalex.org/W2900430453","https://openalex.org/W2901410468","https://openalex.org/W2907185187","https://openalex.org/W2912511860","https://openalex.org/W2953473092","https://openalex.org/W2954931085"],"related_works":["https://openalex.org/W3102090019","https://openalex.org/W4390338302","https://openalex.org/W2793442578","https://openalex.org/W2946774663","https://openalex.org/W4232572698","https://openalex.org/W2911792412","https://openalex.org/W4221072066","https://openalex.org/W3091887760","https://openalex.org/W2106647409","https://openalex.org/W2958312716"],"abstract_inverted_index":{"Employees":[0],"leave":[1,46],"an":[2],"organization":[3],"when":[4],"other":[5],"organizations":[6,54,60,80,96],"offer":[7],"better":[8],"opportunities":[9],"than":[10],"their":[11,57,67],"current":[12],"organizations.":[13],"Continuity":[14],"and":[15,17,69,71,124,181],"sustenance":[16],"even":[18],"completion":[19],"of":[20,66,79,108],"jobs":[21],"are":[22,39,81,97],"crucial":[23],"issues":[24],"for":[25,52,73,99,192],"the":[26,35,44,47,53,76,106,157,194],"companies":[27],"not":[28,82],"to":[29,55,63,85,155],"suffer":[30],"financial":[31],"losses.":[32],"Especially":[33],"if":[34],"talented":[36],"employees,":[37],"who":[38],"at":[40],"critical":[41],"positions":[42],"in":[43,89],"companies,":[45],"job,":[48],"it":[49],"becomes":[50],"difficult":[51],"maintain":[56],"businesses.":[58],"Today,":[59],"would":[61],"like":[62],"predict":[64,156],"attrition":[65,110],"employees":[68],"plan":[70],"prepare":[72],"it.":[74],"However,":[75],"HR":[77,121],"departments":[78],"advanced":[83],"enough":[84],"make":[86],"such":[87,129,173],"predictions":[88],"a":[90],"handcrafted":[91],"manner.":[92],"For":[93],"this":[94,116],"reason,":[95],"looking":[98],"new":[100],"systems":[101],"or":[102],"methods":[103,188],"that":[104,185],"automatize":[105],"prediction":[107],"employee":[109,158,195],"utilizing":[111],"data":[112,122,186],"mining":[113,187],"methods.":[114],"In":[115],"study,":[117],"we":[118,164],"use":[119],"IBM":[120],"set":[123],"apply":[125],"different":[126],"classification":[127,171],"methods,":[128],"as":[130,174],"Support":[131],"Vector":[132],"Machine":[133],"(SVM),":[134],"Random":[135],"Forest,":[136],"J48,":[137],"LogitBoost,":[138],"Multilayer":[139],"Perceptron":[140],"(MLP),":[141],"K-Nearest":[142],"Neighbors":[143],"(KNN),":[144],"Linear":[145],"Discriminant":[146],"Analysis":[147],"(LDA),":[148],"Naive":[149],"Bayes,":[150],"Bagging,":[151],"AdaBoost,":[152],"Logistic":[153],"Regression,":[154],"attrition.":[159,196],"Different":[160],"from":[161],"exiting":[162],"studies,":[163],"systematically":[165],"evaluate":[166],"our":[167],"findings":[168],"with":[169],"various":[170],"metrics,":[172],"F-measure,":[175],"Area":[176],"Under":[177],"Curve,":[178],"accuracy,":[179],"sensitivity,":[180],"specificity.":[182],"We":[183],"observe":[184],"can":[189],"be":[190],"useful":[191],"predicting":[193]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
