{"id":"https://openalex.org/W2007646767","doi":"https://doi.org/10.1145/1363686.1363896","title":"Using ambiguity measure feature selection algorithm for support vector machine classifier","display_name":"Using ambiguity measure feature selection algorithm for support vector machine classifier","publication_year":2008,"publication_date":"2008-03-16","ids":{"openalex":"https://openalex.org/W2007646767","doi":"https://doi.org/10.1145/1363686.1363896","mag":"2007646767"},"language":"en","primary_location":{"id":"doi:10.1145/1363686.1363896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1363686.1363896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 ACM symposium on Applied computing","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/A5067779098","display_name":"Saket S. R. Mengle","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Saket S. R. Mengle","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois","institution_ids":["https://openalex.org/I180949307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036610566","display_name":"Nazli Goharian","orcid":null},"institutions":[{"id":"https://openalex.org/I180949307","display_name":"Illinois Institute of Technology","ror":"https://ror.org/037t3ry66","country_code":"US","type":"education","lineage":["https://openalex.org/I180949307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nazli Goharian","raw_affiliation_strings":["Illinois Institute of Technology, Chicago, Illinois"],"affiliations":[{"raw_affiliation_string":"Illinois Institute of Technology, Chicago, Illinois","institution_ids":["https://openalex.org/I180949307"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5067779098"],"corresponding_institution_ids":["https://openalex.org/I180949307"],"apc_list":null,"apc_paid":null,"fwci":2.3463,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.89612521,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"916","last_page":"920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","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/T11550","display_name":"Text and Document Classification Technologies","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/T10057","display_name":"Face and Expression Recognition","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7679430246353149},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7609916925430298},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.743360161781311},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7122476100921631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6976845264434814},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.6464146375656128},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.5893132090568542},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5819059610366821},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5414775013923645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5129342079162598},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5026495456695557},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4295233190059662},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4103865623474121}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7679430246353149},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7609916925430298},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.743360161781311},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7122476100921631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6976845264434814},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.6464146375656128},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.5893132090568542},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5819059610366821},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5414775013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5129342079162598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5026495456695557},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4295233190059662},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4103865623474121},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1363686.1363896","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1363686.1363896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2008 ACM symposium on Applied computing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.145.143","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://ir.iit.edu/publications/downloads/SAC08-SVM.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1537064529","https://openalex.org/W1574862351","https://openalex.org/W1999635750","https://openalex.org/W2018940048","https://openalex.org/W2029296527","https://openalex.org/W2041145971","https://openalex.org/W2119821739","https://openalex.org/W2138229177","https://openalex.org/W2140390894","https://openalex.org/W2142334564","https://openalex.org/W2149684865","https://openalex.org/W2153635508","https://openalex.org/W2158140178","https://openalex.org/W2435251607","https://openalex.org/W2607336911"],"related_works":["https://openalex.org/W2021546954","https://openalex.org/W2249970211","https://openalex.org/W2055840562","https://openalex.org/W2028016548","https://openalex.org/W8815205","https://openalex.org/W1597543867","https://openalex.org/W3023727762","https://openalex.org/W1987766119","https://openalex.org/W1502255606","https://openalex.org/W2407173734"],"abstract_inverted_index":{"With":[0],"the":[1,7,14,40,64,79,82,92,98,109,117,127,130,139,146],"ever-increasing":[2],"number":[3],"of":[4,16,25,43,81,100,129,148],"documents":[5],"on":[6],"web,":[8],"digital":[9],"libraries,":[10],"news":[11],"sources,":[12],"etc.,":[13],"need":[15],"a":[17,69,101],"text":[18,37,60,131],"classifier":[19,51,132],"that":[20,89,106],"can":[21],"classify":[22],"massive":[23],"amount":[24],"data":[26],"is":[27,39,52,72,123],"becoming":[28],"more":[29,73,113],"critical":[30],"and":[31],"difficult.":[32],"The":[33,46],"major":[34],"problem":[35],"in":[36,151],"classification":[38,61],"high":[41],"dimensionality":[42],"feature":[44,86,121,141,155],"space.":[45],"Support":[47],"Vector":[48],"Machine":[49],"(SVM)":[50],"shown":[53],"to":[54,96,134],"perform":[55],"consistently":[56],"better":[57,136],"than":[58,74,114,116,137],"other":[59,75],"algorithms.":[62,76],"However,":[63],"time":[65,111],"taken":[66],"for":[67],"training":[68,110],"SVM":[70],"model":[71],"We":[77,143],"explore":[78],"use":[80],"Ambiguity":[83],"Measure":[84],"(AM)":[85],"selection":[87,122,156],"method":[88],"uses":[90],"only":[91],"most":[93],"unambiguous":[94],"keywords":[95],"predict":[97],"category":[99],"document.":[102],"Our":[103],"analysis":[104],"shows":[105],"AM":[107],"reduces":[108],"by":[112],"50%":[115],"scenario":[118],"when":[119],"no":[120],"used,":[124],"while":[125],"maintaining":[126],"accuracy":[128],"equivalent":[133],"or":[135],"using":[138,158],"whole":[140],"set.":[142],"empirically":[144],"show":[145],"effectiveness":[147],"our":[149],"approach":[150],"outperforming":[152],"seven":[153],"different":[154],"methods":[157],"two":[159],"standard":[160],"benchmark":[161],"datasets.":[162]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}