{"id":"https://openalex.org/W2029431000","doi":"https://doi.org/10.1109/wacv.2013.6475028","title":"Ridge Regression based classifiers for large scale class imbalanced datasets","display_name":"Ridge Regression based classifiers for large scale class imbalanced datasets","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2029431000","doi":"https://doi.org/10.1109/wacv.2013.6475028","mag":"2029431000"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2013.6475028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2013.6475028","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Workshop on Applications of Computer Vision (WACV)","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/A5000111662","display_name":"Devansh Arpit","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Devansh Arpit","raw_affiliation_strings":["Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008934","display_name":"Shuang Wu","orcid":"https://orcid.org/0000-0003-1237-2656"},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuang Wu","raw_affiliation_strings":["Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102227196","display_name":"Pradeep Natarajan","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pradeep Natarajan","raw_affiliation_strings":["Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107484450","display_name":"Rohit Prasad","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rohit Prasad","raw_affiliation_strings":["Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066184920","display_name":"Prem Natarajan","orcid":"https://orcid.org/0000-0002-4386-6651"},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Premkumar Natarajan","raw_affiliation_strings":["Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Speech, Language and Multimedia Business Unit, Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000111662"],"corresponding_institution_ids":["https://openalex.org/I1306686416"],"apc_list":null,"apc_paid":null,"fwci":1.4427,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85501771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"267","last_page":"274"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9995999932289124,"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":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958999752998352,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9585000276565552,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7532353401184082},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7335039973258972},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7219387292861938},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6597301959991455},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6536471843719482},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5591579079627991},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5579031705856323},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.524041473865509},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5001351833343506},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4947904944419861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3250134289264679},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1167820394039154}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7532353401184082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7335039973258972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7219387292861938},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6597301959991455},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6536471843719482},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5591579079627991},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5579031705856323},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.524041473865509},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5001351833343506},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4947904944419861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3250134289264679},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1167820394039154},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2013.6475028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2013.6475028","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Workshop on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306116","display_name":"U.S. Department of the Interior","ror":"https://ror.org/03v0pmy70"},{"id":"https://openalex.org/F4320333452","display_name":"Interior Business Center","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W102369970","https://openalex.org/W167016754","https://openalex.org/W1736339626","https://openalex.org/W1981813580","https://openalex.org/W1993220166","https://openalex.org/W2011376672","https://openalex.org/W2027717478","https://openalex.org/W2045042261","https://openalex.org/W2056392803","https://openalex.org/W2056614917","https://openalex.org/W2085306757","https://openalex.org/W2085400714","https://openalex.org/W2090042335","https://openalex.org/W2099550922","https://openalex.org/W2104167780","https://openalex.org/W2118978333","https://openalex.org/W2129812935","https://openalex.org/W2142194269","https://openalex.org/W2148143831","https://openalex.org/W2155490028","https://openalex.org/W2162915993","https://openalex.org/W2165837041","https://openalex.org/W2171141701","https://openalex.org/W4233372644","https://openalex.org/W6603183647","https://openalex.org/W6604223235","https://openalex.org/W6606837198","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2747895175","https://openalex.org/W2614669534","https://openalex.org/W2104406636","https://openalex.org/W2123014508","https://openalex.org/W2105512057","https://openalex.org/W4252625449","https://openalex.org/W3131201603","https://openalex.org/W2115788596","https://openalex.org/W4387784741","https://openalex.org/W2741028958"],"abstract_inverted_index":{"Large":[0],"scale,":[1],"class":[2],"imbalanced":[3],"data":[4,56],"classification":[5,60],"is":[6,47],"a":[7],"challenging":[8],"task":[9,52],"that":[10,32,66,130],"occurs":[11],"frequently":[12],"in":[13,30,50],"several":[14],"computer":[15],"vision":[16],"tasks":[17],"such":[18],"as":[19,101],"web":[20,112],"video":[21],"retrieval.":[22],"A":[23],"number":[24],"of":[25,98],"algorithms":[26,61,79],"have":[27],"been":[28],"proposed":[29],"literature":[31],"approach":[33,108],"this":[34,51],"problem":[35],"from":[36,73,114],"different":[37,95],"perspectives":[38],"(e.g.":[39],"Sampling,":[40],"Cost-sensitive":[41],"learning,":[42],"Active":[43],"learning).":[44],"The":[45],"challenge":[46],"two":[48,90,94],"fold":[49],"-":[53],"first":[54],"the":[55,74,118],"imbalance":[57],"causes":[58],"many":[59,78],"to":[62,71,83,151],"learn":[63],"trivial":[64],"classifiers":[65,141],"declare":[67],"all":[68],"test":[69],"examples":[70],"be":[72],"majority":[75],"class.":[76],"Second,":[77],"do":[80],"not":[81],"scale":[82],"large":[84],"dataset":[85,123],"sizes.":[86],"We":[87,105,127],"address":[88],"these":[89],"issues":[91],"by":[92],"using":[93,142],"cost-sensitive":[96],"versions":[97],"Ridge":[99],"Regression":[100],"our":[102,107],"binary":[103],"classifiers.":[104],"demonstrate":[106],"for":[109],"retrieving":[110],"unstructured":[111],"videos":[113],"10":[115],"events":[116],"on":[117],"benchmark":[119],"TRECVID":[120],"MED":[121],"12":[122],"containing":[124],"\u224847000":[125],"videos.":[126],"empirically":[128],"show":[129],"they":[131],"perform":[132],"at":[133],"par":[134],"with":[135],"state-of-the-art":[136],"support":[137],"vector":[138],"machine":[139],"based":[140],"\u03c7":[143],"<sup":[144],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[145],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[146],"kernels":[147],"while":[148],"being":[149],"30":[150],"60":[152],"times":[153],"faster.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}