{"id":"https://openalex.org/W3210997809","doi":"https://doi.org/10.1109/itsc48978.2021.9565059","title":"Rail transit fault text classification based on the latent dirichlet allocation","display_name":"Rail transit fault text classification based on the latent dirichlet allocation","publication_year":2021,"publication_date":"2021-09-19","ids":{"openalex":"https://openalex.org/W3210997809","doi":"https://doi.org/10.1109/itsc48978.2021.9565059","mag":"3210997809"},"language":"en","primary_location":{"id":"doi:10.1109/itsc48978.2021.9565059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103127011","display_name":"Runmei Li","orcid":"https://orcid.org/0000-0002-9034-1399"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"R. Li","raw_affiliation_strings":["Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038996257","display_name":"Shuai Su","orcid":"https://orcid.org/0000-0001-8412-9853"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"S. Su","raw_affiliation_strings":["Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036663288","display_name":"G. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"G. Wang","raw_affiliation_strings":["Department of Computer Science, Rutgers University, Piscataway, New Jersey, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers University, Piscataway, New Jersey, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097302","display_name":"Jiantao Qu","orcid":"https://orcid.org/0000-0002-8664-2236"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"J. Qu","raw_affiliation_strings":["Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frontiers Science Center for Smart High-speed Railway System, the State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, P. R. China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015347934","display_name":"Yuan Cao","orcid":"https://orcid.org/0000-0001-6631-4908"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Y. Cao","raw_affiliation_strings":["School of Electronic and Information Engineering&#x0027;, Beijing Jiaotong University, Beijing, China",", Beijing Jiaotong University, Beijing, China","School of Electronic and Information Engineering&#x0027"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering&#x0027;, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":", Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Electronic and Information Engineering&#x0027","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1359","last_page":"1364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9710999727249146,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8707404136657715},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7029390335083008},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5977188348770142},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5828251838684082},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5205139517784119},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4898628890514374},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.442936509847641},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34918612241744995}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8707404136657715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7029390335083008},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5977188348770142},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5828251838684082},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5205139517784119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4898628890514374},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.442936509847641},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34918612241744995}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc48978.2021.9565059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9565059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5799999833106995,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1430188453","display_name":null,"funder_award_id":"SQ2020YFB160702","funder_id":"https://openalex.org/F4320323067","funder_display_name":"State Key Laboratory of Rail Traffic Control and Safety"}],"funders":[{"id":"https://openalex.org/F4320323067","display_name":"State Key Laboratory of Rail Traffic Control and Safety","ror":"https://ror.org/01yj56c84"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1880262756","https://openalex.org/W1920042757","https://openalex.org/W2024694970","https://openalex.org/W2075006521","https://openalex.org/W2122343331","https://openalex.org/W2340877487","https://openalex.org/W2557478498","https://openalex.org/W2621148700","https://openalex.org/W2791715942","https://openalex.org/W2902435881","https://openalex.org/W2903719424","https://openalex.org/W2976327234","https://openalex.org/W3134035453","https://openalex.org/W3139957115","https://openalex.org/W3159843711","https://openalex.org/W3159954685","https://openalex.org/W3173493645","https://openalex.org/W3179145845","https://openalex.org/W4231510805","https://openalex.org/W6639619044","https://openalex.org/W6738837449"],"related_works":["https://openalex.org/W2888805565","https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W4293863151","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W3005513013"],"abstract_inverted_index":{"A":[0],"large":[1],"amount":[2],"of":[3,28,47,74,82,107,120,157,188],"text":[4,26,40,63,109],"data":[5,83,187],"recorded":[6],"in":[7,55,79,131,146],"rail":[8],"transit":[9],"fault":[10,25,62,135,169,186],"diagnosis":[11],"has":[12],"not":[13,53],"been":[14],"well":[15],"utilized":[16],"at":[17],"present.":[18],"Designing":[19],"a":[20,92],"reasonable":[21],"algorithm":[22],"to":[23,31,125,152,194],"classify":[24],"is":[27,52,84,123,140,182],"great":[29],"significance":[30],"unified":[32],"management":[33],"and":[34,50,134,167],"scheduling":[35],"decision-making.":[36],"However,":[37],"the":[38,44,48,61,104,108,113,121,127,132,138,143,147,154,158,162,165,168,185,189,199],"current":[39],"classification":[41,94],"rarely":[42],"considers":[43],"potential":[45],"meaning":[46],"text,":[49],"it":[51],"effective":[54],"dealing":[56],"with":[57,142,161,184],"synonyms.":[58],"In":[59],"practice,":[60],"can":[64],"be":[65],"regarded":[66],"as":[67],"unbalanced":[68],"small":[69],"sample":[70],"data.":[71],"The":[72,180],"effect":[73],"support":[75],"vector":[76],"machine":[77],"(SVM)":[78],"this":[80,89],"type":[81],"considerable.":[85],"Given":[86],"on":[87,97],"this,":[88],"paper":[90],"proposes":[91],"SVM":[93],"strategy":[95],"based":[96],"latent":[98],"dirichlet":[99],"allocation":[100],"(LDA),":[101],"which":[102,196],"takes":[103],"topic":[105,155,166,177],"information":[106],"into":[110],"account.":[111],"Initially,":[112],"term":[114],"frequency-inverse":[115],"document":[116,133],"frequency":[117],"(TF-IDF)":[118],"index":[119],"word":[122],"used":[124],"judge":[126],"correlation":[128,139],"between":[129,164],"words":[130],"categories.":[136],"Additionally,":[137],"combined":[141],"gibbs":[144],"sampling":[145],"LDA":[148],"model":[149,181],"training":[150],"process":[151],"infer":[153],"distribution":[156],"text.":[159],"Eventually,":[160],"relationship":[163],"category,":[170],"unique":[171],"classifiers":[172],"are":[173],"set":[174],"for":[175],"each":[176],"by":[178],"clustering.":[179],"validated":[183],"Beijing":[190],"metro":[191],"from":[192],"2017":[193],"2020,":[195],"shows":[197],"that":[198],"proposed":[200],"approach":[201],"outperforms":[202],"traditional":[203],"approaches.":[204]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}
