{"id":"https://openalex.org/W2124828870","doi":"https://doi.org/10.1109/ijcnn.2005.1555826","title":"Improved spam e-mail filtering based on committee machines and information theoretic feature extraction","display_name":"Improved spam e-mail filtering based on committee machines and information theoretic feature extraction","publication_year":2006,"publication_date":"2006-01-05","ids":{"openalex":"https://openalex.org/W2124828870","doi":"https://doi.org/10.1109/ijcnn.2005.1555826","mag":"2124828870"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2005.1555826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1555826","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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/A5049201159","display_name":"Vasilios Zorkadis","orcid":null},"institutions":[{"id":"https://openalex.org/I231025917","display_name":"Hellenic Open University","ror":"https://ror.org/02kq26x23","country_code":"GR","type":"education","lineage":["https://openalex.org/I231025917"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"V. Zorkadis","raw_affiliation_strings":["Data Protection Authority, Hellenic Open University, Greece"],"affiliations":[{"raw_affiliation_string":"Data Protection Authority, Hellenic Open University, Greece","institution_ids":["https://openalex.org/I231025917"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047033768","display_name":"M. Panayotou","orcid":null},"institutions":[{"id":"https://openalex.org/I231025917","display_name":"Hellenic Open University","ror":"https://ror.org/02kq26x23","country_code":"GR","type":"education","lineage":["https://openalex.org/I231025917"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"M. Panayotou","raw_affiliation_strings":["Hellenic Open University, Greece"],"affiliations":[{"raw_affiliation_string":"Hellenic Open University, Greece","institution_ids":["https://openalex.org/I231025917"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002850928","display_name":"D.A. Karras","orcid":"https://orcid.org/0000-0002-2759-8482"},"institutions":[{"id":"https://openalex.org/I231025917","display_name":"Hellenic Open University","ror":"https://ror.org/02kq26x23","country_code":"GR","type":"education","lineage":["https://openalex.org/I231025917"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"D.A. Karras","raw_affiliation_strings":["Department Automation and Hellenic Open University, Chalkis Institute of Technology, Athens, Greece"],"affiliations":[{"raw_affiliation_string":"Department Automation and Hellenic Open University, Chalkis Institute of Technology, Athens, Greece","institution_ids":["https://openalex.org/I231025917"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049201159"],"corresponding_institution_ids":["https://openalex.org/I231025917"],"apc_list":null,"apc_paid":null,"fwci":2.8418,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.9183147,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"2","issue":null,"first_page":"179","last_page":"184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11644","display_name":"Spam and Phishing Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9987000226974487,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7820913791656494},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6883608102798462},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.663790225982666},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6370879411697388},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5951290726661682},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5771926045417786},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5482625365257263},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5248686671257019},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5046993494033813},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4735024571418762},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.4392361640930176},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.43053707480430603},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3720182776451111},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.29796186089515686}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7820913791656494},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6883608102798462},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.663790225982666},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6370879411697388},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5951290726661682},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5771926045417786},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5482625365257263},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5248686671257019},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5046993494033813},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4735024571418762},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.4392361640930176},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.43053707480430603},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3720182776451111},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29796186089515686},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2005.1555826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1555826","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1504008138","https://openalex.org/W1598333443","https://openalex.org/W1881647329","https://openalex.org/W2067553552","https://openalex.org/W2098162425","https://openalex.org/W2099111195","https://openalex.org/W2120011452","https://openalex.org/W2124776405","https://openalex.org/W2141828330","https://openalex.org/W2169384781","https://openalex.org/W4254582343","https://openalex.org/W4285719527","https://openalex.org/W6630221274","https://openalex.org/W6639409548","https://openalex.org/W6677773337","https://openalex.org/W6680748510"],"related_works":["https://openalex.org/W1487671059","https://openalex.org/W4239608116","https://openalex.org/W2002382339","https://openalex.org/W2076543106","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063"],"abstract_inverted_index":{"A":[0],"novel":[1],"approach":[2,128],"for":[3],"spam":[4,54,120,149],"e-mail":[5,43,131],"filtering":[6],"is":[7,28,98,107,123],"herein":[8],"considered":[9],"based":[10,38,67,76,91],"on":[11,19,39,92],"the":[12,30,36,47,51,60,64,73,101,112,118,124,130,139],"committee":[13,102],"machines":[14,103],"neural":[15,80],"network":[16,81],"models":[17,66,75],"and":[18,72],"information":[20,93,141],"theoretic":[21,142],"feature":[22,88],"extraction.":[23],"An":[24],"extensive":[25,32],"experimental":[26],"study":[27],"organized,":[29],"most":[31,125],"so":[33],"far":[34],"in":[35,129],"literature,":[37],"widely":[40,126],"accepted":[41],"benchmarking":[42],"data":[44],"sets,":[45],"comparing":[46],"proposed":[48,140],"methodology":[49],"with":[50,59],"naive":[52],"Bayes":[53,119],"filter":[55,121],"as":[56,58],"well":[57],"boosting":[61],"tree":[62],"methodology,":[63],"linear":[65],"classification":[68,77],"(classification":[69],"via":[70],"regression)":[71],"nonlinear":[74],"using":[78],"simple":[79],"models,":[82],"including":[83,117],"multilayer":[84],"perceptrons.":[85],"Moreover,":[86],"several":[87],"extraction":[89],"approaches":[90],"theory":[94],"are":[95],"evaluated.":[96],"It":[97,134],"shown":[99],"that":[100,138],"mail":[104],"categorization":[105,150],"performance":[106,151],"compared":[108,152],"very":[109],"favorably":[110],"to":[111,153],"other":[113],"rival":[114],"methods":[115],"performance,":[116],"which":[122],"used":[127],"services":[132],"market.":[133],"is,":[135],"also,":[136],"found":[137],"Boolean":[143],"features":[144],"present":[145],"a":[146],"remarkably":[147],"high":[148],"their":[154],"analog":[155],"counterparts":[156],"performance.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}
