{"id":"https://openalex.org/W2538507065","doi":"https://doi.org/10.1109/icdim.2016.7829755","title":"HSDD: A hybrid sampling strategy for class imbalance in defect prediction data sets","display_name":"HSDD: A hybrid sampling strategy for class imbalance in defect prediction data sets","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2538507065","doi":"https://doi.org/10.1109/icdim.2016.7829755","mag":"2538507065"},"language":"en","primary_location":{"id":"doi:10.1109/icdim.2016.7829755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdim.2016.7829755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Eleventh International Conference on Digital Information Management (ICDIM)","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/A5055357850","display_name":"Muhammed Maruf \u00d6zt\u00fcrk","orcid":"https://orcid.org/0000-0001-6446-9754"},"institutions":[{"id":"https://openalex.org/I103703290","display_name":"Sakarya University","ror":"https://ror.org/04ttnw109","country_code":"TR","type":"education","lineage":["https://openalex.org/I103703290"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"M. Maruf Ozturk","raw_affiliation_strings":["Department of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya, TURKEY"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya, TURKEY","institution_ids":["https://openalex.org/I103703290"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014273833","display_name":"Ahmet Zengi\u0307n","orcid":"https://orcid.org/0000-0003-0384-4148"},"institutions":[{"id":"https://openalex.org/I103703290","display_name":"Sakarya University","ror":"https://ror.org/04ttnw109","country_code":"TR","type":"education","lineage":["https://openalex.org/I103703290"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Ahmet Zengin","raw_affiliation_strings":["Department of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya, TURKEY"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya, TURKEY","institution_ids":["https://openalex.org/I103703290"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055357850"],"corresponding_institution_ids":["https://openalex.org/I103703290"],"apc_list":null,"apc_paid":null,"fwci":1.2854,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.87249468,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"28","issue":null,"first_page":"225","last_page":"234"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9975000023841858,"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":0.9975000023841858,"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/computer-science","display_name":"Computer science","score":0.6529747843742371},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6220661401748657},{"id":"https://openalex.org/keywords/c4.5-algorithm","display_name":"C4.5 algorithm","score":0.618956983089447},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5544878840446472},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5321043729782104},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.50716233253479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5026609897613525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5018231868743896},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4603523910045624},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4349944591522217},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36208608746528625},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23546943068504333},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1711294949054718},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12290218472480774}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529747843742371},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6220661401748657},{"id":"https://openalex.org/C52003472","wikidata":"https://www.wikidata.org/wiki/Q1022655","display_name":"C4.5 algorithm","level":4,"score":0.618956983089447},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5544878840446472},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5321043729782104},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.50716233253479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5026609897613525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5018231868743896},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4603523910045624},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4349944591522217},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36208608746528625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23546943068504333},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1711294949054718},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12290218472480774},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdim.2016.7829755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdim.2016.7829755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 Eleventh International Conference on Digital Information Management (ICDIM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W168970045","https://openalex.org/W1641872551","https://openalex.org/W1708892938","https://openalex.org/W1969483024","https://openalex.org/W1976526581","https://openalex.org/W1991181258","https://openalex.org/W1994493193","https://openalex.org/W2007797768","https://openalex.org/W2020458104","https://openalex.org/W2038464048","https://openalex.org/W2038917214","https://openalex.org/W2081697244","https://openalex.org/W2094947835","https://openalex.org/W2120457925","https://openalex.org/W2122195835","https://openalex.org/W2135074661","https://openalex.org/W2148143831","https://openalex.org/W2158698691","https://openalex.org/W2163732854","https://openalex.org/W2164627280","https://openalex.org/W2172232422","https://openalex.org/W2172611079","https://openalex.org/W2188047397","https://openalex.org/W2548061775","https://openalex.org/W2606554049","https://openalex.org/W6637492169","https://openalex.org/W6677881769","https://openalex.org/W6682141768"],"related_works":["https://openalex.org/W2997511728","https://openalex.org/W4214653893","https://openalex.org/W2149097950","https://openalex.org/W3124731355","https://openalex.org/W3036853918","https://openalex.org/W3211372245","https://openalex.org/W2505282017","https://openalex.org/W3013263334","https://openalex.org/W2995276271","https://openalex.org/W2537862391"],"abstract_inverted_index":{"Class":[0],"imbalance":[1,51],"is":[2,42],"a":[3],"common":[4],"problem":[5],"in":[6,52,107,124],"defect":[7,53,63],"prediction":[8,54],"data":[9,55,64,84,96,126],"sets.":[10,56,127],"In":[11],"order":[12],"to":[13,47,66],"cope":[14,48],"with":[15,49],"this":[16,68],"problem,":[17],"over-sampling":[18],"and":[19,34,92,105,111],"under":[20],"sampling":[21,61],"methods":[22,27],"are":[23,28],"employed.":[24],"However,":[25],"these":[26],"designed":[29],"for":[30,37,62],"instance":[31],"based":[32],"alteration":[33],"not":[35,43,72],"specialized":[36],"feature":[38],"space.":[39],"Also":[40],"there":[41],"any":[44],"distinctive":[45],"approach":[46],"class":[50],"We":[57],"develop":[58],"HSDD":[59,70,118],"(hybrid":[60],"sets)":[65],"solve":[67],"problem.":[69],"comprises":[71],"only":[73],"derivation":[74],"of":[75,82,109],"low-level":[76],"metrics,":[77],"but":[78],"also":[79],"reduction":[80],"processes":[81],"repeated":[83],"points.":[85],"The":[86],"method":[87],"was":[88],"evaluated":[89],"on":[90],"industrial":[91],"open":[93],"source":[94],"project":[95],"sets":[97],"by":[98],"using":[99],"Bayes,":[100,102],"naive":[101],"random":[103],"forest,":[104],"J48":[106],"terms":[108],"g-mean":[110],"training":[112,121],"time.":[113],"Obtained":[114],"results":[115],"show":[116],"that":[117],"produces":[119],"promising":[120],"performance":[122],"especially":[123],"large-scale":[125]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}