{"id":"https://openalex.org/W2943519671","doi":"https://doi.org/10.23919/icact.2019.8702043","title":"Improving K Nearest Neighbor into String Vector Version for Text Categorization","display_name":"Improving K Nearest Neighbor into String Vector Version for Text Categorization","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2943519671","doi":"https://doi.org/10.23919/icact.2019.8702043","mag":"2943519671"},"language":"en","primary_location":{"id":"doi:10.23919/icact.2019.8702043","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icact.2019.8702043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 21st International Conference on Advanced Communication Technology (ICACT)","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/A5049496911","display_name":"Taeho Jo","orcid":"https://orcid.org/0000-0001-7448-9433"},"institutions":[{"id":"https://openalex.org/I94588446","display_name":"Hongik University","ror":"https://ror.org/00egdv862","country_code":"KR","type":"education","lineage":["https://openalex.org/I94588446"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Taeho Jo","raw_affiliation_strings":["School of Game, Hongik University, Sejong, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"School of Game, Hongik University, Sejong, Republic of Korea","institution_ids":["https://openalex.org/I94588446"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049496911"],"corresponding_institution_ids":["https://openalex.org/I94588446"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.64021897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1091","last_page":"1097"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9997000098228455,"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":0.9997000098228455,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9980000257492065,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6682361364364624},{"id":"https://openalex.org/keywords/string","display_name":"String (physics)","score":0.6527664661407471},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.58124178647995},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5648541450500488},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5026860237121582},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4998185634613037},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.479361355304718},{"id":"https://openalex.org/keywords/text-categorization","display_name":"Text categorization","score":0.4571549594402313},{"id":"https://openalex.org/keywords/string-metric","display_name":"String metric","score":0.44518256187438965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4445438086986542},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.41341841220855713},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3433082103729248},{"id":"https://openalex.org/keywords/string-searching-algorithm","display_name":"String searching algorithm","score":0.3149109482765198},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.239711731672287},{"id":"https://openalex.org/keywords/pattern-matching","display_name":"Pattern matching","score":0.0961163341999054}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682361364364624},{"id":"https://openalex.org/C157486923","wikidata":"https://www.wikidata.org/wiki/Q1376436","display_name":"String (physics)","level":2,"score":0.6527664661407471},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.58124178647995},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5648541450500488},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5026860237121582},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4998185634613037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.479361355304718},{"id":"https://openalex.org/C2986744138","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Text categorization","level":3,"score":0.4571549594402313},{"id":"https://openalex.org/C22820288","wikidata":"https://www.wikidata.org/wiki/Q9050568","display_name":"String metric","level":4,"score":0.44518256187438965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4445438086986542},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.41341841220855713},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3433082103729248},{"id":"https://openalex.org/C7757238","wikidata":"https://www.wikidata.org/wiki/Q374040","display_name":"String searching algorithm","level":3,"score":0.3149109482765198},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.239711731672287},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.0961163341999054},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/icact.2019.8702043","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icact.2019.8702043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 21st International Conference on Advanced Communication Technology (ICACT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321237","display_name":"Hongik University","ror":"https://ror.org/00egdv862"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1508079937","https://openalex.org/W1646134777","https://openalex.org/W1980483130","https://openalex.org/W1991122732","https://openalex.org/W2018423466","https://openalex.org/W2118020653","https://openalex.org/W2127221278","https://openalex.org/W2140266767","https://openalex.org/W2170960297","https://openalex.org/W2878746262","https://openalex.org/W3026106150","https://openalex.org/W6630219245","https://openalex.org/W6636811164","https://openalex.org/W6777334961"],"related_works":["https://openalex.org/W2375795576","https://openalex.org/W2986277685","https://openalex.org/W2519145296","https://openalex.org/W2590193441","https://openalex.org/W2187092961","https://openalex.org/W2187002734","https://openalex.org/W2257399947","https://openalex.org/W2051764263","https://openalex.org/W2595827536","https://openalex.org/W2054173104"],"abstract_inverted_index":{"This":[0],"research":[1,59,119],"is":[2,14,75,80,88,96,120],"concerned":[3],"with":[4,102],"the":[5,11,15,18,31,41,56,69,78,83,103,108,123],"string":[6,67,73,86],"vector":[7,87],"based":[8],"version":[9,33,84,95,106],"of":[10,34,117],"KNN":[12,79,94,105],"which":[13],"approach":[16],"to":[17,40,54,121],"text":[19,124],"categorization.":[20],"Traditionally,":[21],"texts":[22,62],"have":[23],"been":[24],"encoded":[25,65],"into":[26,66,82],"numerical":[27],"vectors":[28,68,74],"for":[29],"using":[30],"traditional":[32,104],"KNN,":[35],"and":[36,49,77,113],"encoding":[37],"so":[38],"leads":[39],"three":[42,109],"main":[43],"problems:":[44],"huge":[45],"dimensionality,":[46],"sparse":[47],"distribution,":[48],"poor":[50],"transparency.":[51],"In":[52],"order":[53],"solve":[55],"problems,":[57],"this":[58,118],"propose":[60],"that":[61],"should":[63],"be":[64],"similarity":[70],"measure":[71],"between":[72],"defined,":[76],"modified":[81],"where":[85],"given":[89],"its":[90],"input.":[91],"The":[92,115],"proposed":[93],"validated":[97],"empirically":[98],"by":[99,127],"comparing":[100],"it":[101],"on":[107],"collections:":[110],"NewsPage.com,":[111],"Opiniopsis,":[112],"20NewsGroups.":[114],"goal":[116],"improve":[122],"categorization":[125],"performance":[126],"solving":[128],"them.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":19},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
