{"id":"https://openalex.org/W2108251824","doi":"https://doi.org/10.1109/ccece.2010.5575224","title":"Automated synthesis of feature functions for pattern detection","display_name":"Automated synthesis of feature functions for pattern detection","publication_year":2010,"publication_date":"2010-05-01","ids":{"openalex":"https://openalex.org/W2108251824","doi":"https://doi.org/10.1109/ccece.2010.5575224","mag":"2108251824"},"language":"en","primary_location":{"id":"doi:10.1109/ccece.2010.5575224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccece.2010.5575224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CCECE 2010","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/A5079247627","display_name":"Peifang Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Pei-Fang Guo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","[Department of Electrical and Computer Engineering, Concordia University, Montr\u00e9al, Canada]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, Concordia University, Montr\u00e9al, Canada]","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111872056","display_name":"Prabir Bhattacharya","orcid":null},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prabir Bhattacharya","raw_affiliation_strings":["Department of Computer Science, University of Cincinnati, Cincinnati, USA","[Department of Computer Science, University of Cincinnati, Cincinnati, USA]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Cincinnati, Cincinnati, USA","institution_ids":["https://openalex.org/I63135867"]},{"raw_affiliation_string":"[Department of Computer Science, University of Cincinnati, Cincinnati, USA]","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007362775","display_name":"Nawwaf Kharma","orcid":"https://orcid.org/0000-0003-3399-3902"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nawwaf Kharma","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","[Department of Electrical and Computer Engineering, Concordia University, Montr\u00e9al, Canada]"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"[Department of Electrical and Computer Engineering, Concordia University, Montr\u00e9al, Canada]","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5079247627"],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":0.451,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.7389675,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9983000159263611,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9983000159263611,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9977999925613403,"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/T10320","display_name":"Neural Networks and Applications","score":0.9936000108718872,"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/computer-science","display_name":"Computer science","score":0.7075968980789185},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6652370691299438},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.6386486291885376},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6236141920089722},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5940520763397217},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5498318076133728},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5334790945053101},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5048416256904602},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.49921298027038574},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47995665669441223},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.4368303716182709},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.43412482738494873},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.14282605051994324}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7075968980789185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6652370691299438},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.6386486291885376},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6236141920089722},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5940520763397217},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5498318076133728},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5334790945053101},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5048416256904602},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.49921298027038574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47995665669441223},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.4368303716182709},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.43412482738494873},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.14282605051994324},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccece.2010.5575224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccece.2010.5575224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CCECE 2010","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1517016597","https://openalex.org/W1663973292","https://openalex.org/W1979894912","https://openalex.org/W1982384746","https://openalex.org/W2009086942","https://openalex.org/W2084705412","https://openalex.org/W2084812512","https://openalex.org/W2097932601","https://openalex.org/W2133874182","https://openalex.org/W2162473406","https://openalex.org/W2799061466","https://openalex.org/W4239875977"],"related_works":["https://openalex.org/W2370362751","https://openalex.org/W2091456498","https://openalex.org/W2149434756","https://openalex.org/W2055840562","https://openalex.org/W151646368","https://openalex.org/W2028016548","https://openalex.org/W2162233951","https://openalex.org/W8815205","https://openalex.org/W1597543867","https://openalex.org/W3023727762"],"abstract_inverted_index":{"In":[0],"pattern":[1],"detection":[2],"systems,":[3],"the":[4,53,59,79,83,102,109,124,129],"general":[5],"techniques":[6],"of":[7,22,34,55,92,104],"feature":[8,17,67,94,127,142],"extraction":[9],"and":[10,58,96,118],"selection":[11],"perform":[12,89],"linear":[13,32],"transformations":[14,91],"from":[15],"primitive":[16,36,71,93,126],"vectors":[18,21,95],"to":[19,42,64,88,101],"new":[20,27],"lower":[23],"dimensionality.":[24],"At":[25],"times,":[26],"extracted":[28],"features":[29,37,73],"might":[30],"be":[31],"combinations":[33],"some":[35],"that":[38],"are":[39],"not":[40],"able":[41,87],"provide":[43],"better":[44],"classification":[45],"accuracy.":[46],"To":[47],"solve":[48],"this":[49],"problem,":[50],"we":[51],"propose":[52],"integration":[54],"genetic":[56],"programming":[57],"expectation":[60],"maximization":[61],"algorithm":[62,85,131],"(GP-EM)":[63],"automatically":[65],"synthesize":[66],"functions":[68],"based":[69],"on":[70],"input":[72],"for":[74],"breast":[75],"cancer":[76],"detection.":[77],"With":[78],"Gaussian":[80],"mixture":[81],"model,":[82],"proposed":[84,130],"is":[86],"nonlinear":[90],"data":[97],"modeling":[98],"simultaneously.":[99],"Compared":[100],"performance":[103],"other":[105],"algorithms,":[106],"such":[107],"us":[108],"support":[110],"vector":[111],"machine,":[112],"multi-layer":[113],"perceptrons,":[114],"inductive":[115],"machine":[116],"learning":[117],"logistic":[119],"regression,":[120],"which":[121],"all":[122],"used":[123],"entire":[125],"set,":[128],"achieves":[132],"a":[133],"higher":[134],"recognition":[135],"rate":[136],"by":[137],"using":[138],"one":[139],"single":[140],"synthesized":[141],"function.":[143]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
