{"id":"https://openalex.org/W2002224150","doi":"https://doi.org/10.1080/09528130600975873","title":"Efficient information theoretic extraction of higher order features for improving neural network-based spam e-mail categorization","display_name":"Efficient information theoretic extraction of higher order features for improving neural network-based spam e-mail categorization","publication_year":2006,"publication_date":"2006-10-20","ids":{"openalex":"https://openalex.org/W2002224150","doi":"https://doi.org/10.1080/09528130600975873","mag":"2002224150"},"language":"en","primary_location":{"id":"doi:10.1080/09528130600975873","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09528130600975873","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental & Theoretical Artificial Intelligence","raw_type":"journal-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":false,"raw_author_name":"V. Zorkadis","raw_affiliation_strings":["Data Protection Authority and Hellenic Open University , Greece"],"affiliations":[{"raw_affiliation_string":"Data Protection Authority and 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":true,"raw_author_name":"D. A. Karras","raw_affiliation_strings":["Chalkis Institute of Technology, Dept. Automation and Hellenic Open University Rodu 2 , Ano Iliupolis, Athens 16342, Greece"],"affiliations":[{"raw_affiliation_string":"Chalkis Institute of Technology, Dept. Automation and Hellenic Open University Rodu 2 , Ano Iliupolis, Athens 16342, Greece","institution_ids":["https://openalex.org/I231025917"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002850928"],"corresponding_institution_ids":["https://openalex.org/I231025917"],"apc_list":null,"apc_paid":null,"fwci":0.8208,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82927552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"18","issue":"4","first_page":"523","last_page":"534"},"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":0.9990000128746033,"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":0.9972000122070312,"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":0.853920578956604},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6247356534004211},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6216996908187866},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6053479909896851},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.571270763874054},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5643191337585449},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5459080338478088},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5138871669769287},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.48957809805870056},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48119813203811646},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4726088345050812},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.43032270669937134},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34306013584136963},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2160913050174713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.853920578956604},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6247356534004211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6216996908187866},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6053479909896851},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.571270763874054},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5643191337585449},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5459080338478088},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5138871669769287},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.48957809805870056},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48119813203811646},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4726088345050812},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.43032270669937134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34306013584136963},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2160913050174713},{"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:10.1080/09528130600975873","is_oa":false,"landing_page_url":"https://doi.org/10.1080/09528130600975873","pdf_url":null,"source":{"id":"https://openalex.org/S153467142","display_name":"Journal of Experimental & Theoretical Artificial Intelligence","issn_l":"0952-813X","issn":["0952-813X","1362-3079"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Experimental & Theoretical Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"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/W2169384781","https://openalex.org/W6639409548"],"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/W2775696145","https://openalex.org/W2523437662","https://openalex.org/W4387048144","https://openalex.org/W2492135063"],"abstract_inverted_index":{"A":[0],"novel":[1],"approach":[2,176],"for":[3],"spam":[4,56,135,168],"e-mail":[5,45,179],"filtering":[6],"is":[7,30,114,147,171],"herein":[8],"considered":[9],"based":[10,40,91],"on":[11,41,92],"information":[12,93,107,127],"theoretic":[13,108,128],"extraction":[14,89,104,109],"of":[15,110,141],"higher":[16,101],"order":[17,102],"features":[18,130],"and":[19,73],"the":[20,32,38,49,53,62,66,74,99,117,120,125,150,160,166,172,178],"committee":[21,151],"machines":[22,152],"neural":[23,80],"network":[24,81],"models.":[25],"An":[26],"extensive":[27,34],"experimental":[28],"study":[29],"organized,":[31],"most":[33,173],"so":[35],"far":[36],"in":[37,177],"literature,":[39],"widely":[42,174],"accepted":[43],"benchmarking":[44],"data":[46],"sets,":[47],"comparing":[48,97],"proposed":[50,100,126],"methodology":[51,105],"with":[52,61,106],"Na\u00efve":[54],"Bayes":[55,167],"filter":[57,169],"as":[58,60],"well":[59],"Boosting":[63],"tree":[64],"methodology,":[65],"linear":[67],"models-based":[68,76],"classification":[69,77],"(classification":[70],"via":[71],"regression)":[72],"nonlinear":[75],"using":[78],"simple":[79],"models,":[82],"including":[83,165],"Multilayer":[84],"Perceptrons.":[85],"Moreover,":[86],"several":[87],"feature":[88,103],"approaches":[90],"theory":[94],"are":[95],"evaluated,":[96],"mainly":[98],"single":[111],"features.":[112],"It":[113],"shown":[115,148],"that":[116,124,140,149],"former":[118],"outperforms":[119],"latter":[121],"and,":[122],"moreover,":[123],"Boolean":[129],"present":[131],"a":[132],"remarkably":[133],"high":[134],"categorization":[136,154],"performance":[137,155],"compared":[138],"to":[139,159],"their":[142],"analog":[143],"counterparts.":[144],"Finally,":[145],"it":[146],"mail":[153],"compares":[156],"very":[157],"favorably":[158],"other":[161],"rival":[162],"methods'":[163],"performance,":[164],"which":[170],"used":[175],"services":[180],"market.":[181]},"counts_by_year":[{"year":2015,"cited_by_count":3},{"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"}