{"id":"https://openalex.org/W3042256070","doi":"https://doi.org/10.3233/jifs-192064","title":"Construct a biased SVM classifier based on Chebyshev distance for PU learning","display_name":"Construct a biased SVM classifier based on Chebyshev distance for PU learning","publication_year":2020,"publication_date":"2020-07-14","ids":{"openalex":"https://openalex.org/W3042256070","doi":"https://doi.org/10.3233/jifs-192064","mag":"3042256070"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-192064","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-192064","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy 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/A5016896170","display_name":"Ting Ke","orcid":"https://orcid.org/0000-0003-4504-4827"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ting Ke","raw_affiliation_strings":["Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]},{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100719013","display_name":"Min Li","orcid":"https://orcid.org/0000-0002-0188-1394"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Min Li","raw_affiliation_strings":["Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]},{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416417","display_name":"Lidong Zhang","orcid":"https://orcid.org/0000-0002-0758-2475"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lidong Zhang","raw_affiliation_strings":["Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]},{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065721557","display_name":"Hui Lv","orcid":"https://orcid.org/0000-0002-4337-6679"},"institutions":[{"id":"https://openalex.org/I132369690","display_name":"Tianjin University of Science and Technology","ror":"https://ror.org/018rbtf37","country_code":"CN","type":"education","lineage":["https://openalex.org/I132369690"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Lv","raw_affiliation_strings":["Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]},{"raw_affiliation_string":"Department of Mathematics, College of Science, Tianjin University of Science & Technology, Tianjin, China","institution_ids":["https://openalex.org/I132369690"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061268775","display_name":"Xuechun Ge","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuechun Ge","raw_affiliation_strings":["China Academy of Railway Sciences Signal and Communication Research Institute (Beijing Huatie Information Technology Corporation), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Railway Sciences Signal and Communication Research Institute (Beijing Huatie Information Technology Corporation), Beijing, China","institution_ids":["https://openalex.org/I4210141966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016896170"],"corresponding_institution_ids":["https://openalex.org/I132369690"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55012859,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"39","issue":"3","first_page":"3749","last_page":"3767"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9991999864578247,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9944999814033508,"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/support-vector-machine","display_name":"Support vector machine","score":0.6964119672775269},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6373866200447083},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6101400256156921},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5860474705696106},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.5377495288848877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5076159238815308},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4621274173259735},{"id":"https://openalex.org/keywords/chebyshev-filter","display_name":"Chebyshev filter","score":0.44435614347457886},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4142563045024872},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39898672699928284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3848755955696106},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37932249903678894},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3019677996635437},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13002443313598633}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6964119672775269},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6373866200447083},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6101400256156921},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5860474705696106},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.5377495288848877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5076159238815308},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4621274173259735},{"id":"https://openalex.org/C21424316","wikidata":"https://www.wikidata.org/wiki/Q718621","display_name":"Chebyshev filter","level":2,"score":0.44435614347457886},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4142563045024872},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39898672699928284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3848755955696106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37932249903678894},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3019677996635437},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13002443313598633},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-192064","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-192064","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W3310308","https://openalex.org/W1530558387","https://openalex.org/W1538189649","https://openalex.org/W1969059608","https://openalex.org/W1970169482","https://openalex.org/W1985520842","https://openalex.org/W1995298481","https://openalex.org/W2016317391","https://openalex.org/W2026906501","https://openalex.org/W2041971917","https://openalex.org/W2068908391","https://openalex.org/W2101029276","https://openalex.org/W2108693588","https://openalex.org/W2123958887","https://openalex.org/W2132870739","https://openalex.org/W2133227149","https://openalex.org/W2134510195","https://openalex.org/W2146807976","https://openalex.org/W2156772624","https://openalex.org/W2156909104","https://openalex.org/W2296988146","https://openalex.org/W2338448737","https://openalex.org/W2616198738","https://openalex.org/W2767250043","https://openalex.org/W2807689990","https://openalex.org/W2907202717","https://openalex.org/W2922210059","https://openalex.org/W2963121529","https://openalex.org/W2963209029","https://openalex.org/W2968414212","https://openalex.org/W3011712950"],"related_works":["https://openalex.org/W4245457074","https://openalex.org/W2315788397","https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W2347733609","https://openalex.org/W2390710574","https://openalex.org/W3004218185","https://openalex.org/W1717256175","https://openalex.org/W4318818647","https://openalex.org/W2904746142"],"abstract_inverted_index":{"In":[0],"some":[1],"real":[2],"applications,":[3],"only":[4],"limited":[5],"labeled":[6,55,92,171,240],"positive":[7,26,56,93],"examples":[8,12,94],"and":[9,27,60,64,128,214,244,253,275],"many":[10],"unlabeled":[11,28],"are":[13,17,52,103,108,185,195],"available,":[14],"but":[15],"there":[16],"no":[18],"negative":[19],"examples.":[20],"Such":[21],"learning":[22,32,158],"is":[23,58,68,83,95,173,258],"termed":[24],"as":[25,265],"(PU)":[29],"learning.":[30],"PU":[31],"algorithm":[33,203],"has":[34],"been":[35],"studied":[36],"extensively":[37],"in":[38,139,157,164,187],"recent":[39],"years.":[40],"However,":[41],"the":[42,47,65,78,81,88,91,98,125,146,165,168,178,181,191,216,219],"classical":[43],"ones":[44],"based":[45],"on":[46,225],"Support":[48],"Vector":[49],"Machines":[50],"(SVMs)":[51],"assumed":[53],"that":[54,184,255],"data":[57,172,241],"independent":[59],"identically":[61],"distributed":[62],"(i.i.d)":[63],"sample":[66,153,182],"size":[67,169],"large":[69,206],"enough.":[70],"It":[71],"leads":[72],"to":[73,123,155,159],"two":[74],"obvious":[75],"shortcomings.":[76],"On":[77,97],"one":[79],"hand,":[80,100],"performance":[82],"not":[84,104],"satisfactory,":[85],"especially":[86,163],"when":[87,106],"number":[89],"of":[90,170,180,218,228,235],"small.":[96],"other":[99],"classification":[101,161],"results":[102],"optimistic":[105],"datasets":[107,248],"Non-i.i.d.":[109],"For":[110],"this":[111,113],"reason,":[112],"paper":[114],"proposes":[115],"a":[116],"novel":[117],"SVM":[118],"classifier":[119,257],"using":[120],"Chebyshev":[121],"distance":[122,179],"measure":[124],"empirical":[126],"risk":[127],"designs":[129],"an":[130],"efficient":[131],"iterative":[132,202],"algorithm,":[133],"named":[134],"L":[135,141],"\u221e":[136,142],"-":[137,143],"BSVM":[138,144],"short.":[140],"includes":[145],"following":[147],"merits:":[148],"(1)":[149],"it":[150,176],"allows":[151],"all":[152],"points":[154,183],"participate":[156],"prompt":[160],"performance,":[162],"case":[166],"where":[167,193],"small;":[174],"(2)":[175],"minimizes":[177],"(outliers":[186],"Non-i.i.d)":[188],"farthest":[189],"from":[190],"hyper-plane,":[192],"outliers":[194],"sufficiently":[196],"taken":[197],"into":[198],"consideration":[199],"(3)":[200],"our":[201,256],"can":[204],"solve":[205],"scale":[207],"optimization":[208],"problem":[209],"with":[210,238],"low":[211],"time":[212],"complexity":[213],"ensure":[215],"convergence":[217],"optimum":[220],"solution.":[221],"Finally,":[222],"extensive":[223],"experiments":[224],"three":[226],"types":[227],"datasets:":[229],"artificial":[230],"Non-i.i.d":[231],"datasets,":[232],"fault":[233],"diagnosis":[234],"railway":[236],"turnout":[237],"few":[239],"(abnormal":[242],"turnout)":[243],"six":[245],"benchmark":[246],"real-world":[247],"verify":[249],"above":[250],"opinions":[251],"again":[252],"demonstrate":[254],"much":[259],"better":[260],"than":[261],"state-of-the-art":[262],"competitors,":[263],"such":[264],"B-SVM":[266],",":[267,269,271,273],"LUHC":[268],"Pulce":[270],"B-LSSVM":[272],"NB":[274],"so":[276],"on.":[277]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}