{"id":"https://openalex.org/W2782556930","doi":"https://doi.org/10.1109/iciinfs.2016.8262978","title":"Novel approach for incremental learning using ensemble of SVMs with particle swarm optimization","display_name":"Novel approach for incremental learning using ensemble of SVMs with particle swarm optimization","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2782556930","doi":"https://doi.org/10.1109/iciinfs.2016.8262978","mag":"2782556930"},"language":"en","primary_location":{"id":"doi:10.1109/iciinfs.2016.8262978","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciinfs.2016.8262978","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 11th International Conference on Industrial and Information Systems (ICIIS)","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/A5101499730","display_name":"Aditya Gupta","orcid":"https://orcid.org/0000-0003-0102-5843"},"institutions":[{"id":"https://openalex.org/I887998513","display_name":"Bharati Vidyapeeth Deemed University","ror":"https://ror.org/0052mmx10","country_code":"IN","type":"education","lineage":["https://openalex.org/I887998513"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Aditya Gupta","raw_affiliation_strings":["Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India","institution_ids":["https://openalex.org/I887998513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017988745","display_name":"Kunal Gusain","orcid":null},"institutions":[{"id":"https://openalex.org/I887998513","display_name":"Bharati Vidyapeeth Deemed University","ror":"https://ror.org/0052mmx10","country_code":"IN","type":"education","lineage":["https://openalex.org/I887998513"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kunal Gusain","raw_affiliation_strings":["Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India","institution_ids":["https://openalex.org/I887998513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066953531","display_name":"Deepika Kumar","orcid":"https://orcid.org/0000-0002-6690-8500"},"institutions":[{"id":"https://openalex.org/I887998513","display_name":"Bharati Vidyapeeth Deemed University","ror":"https://ror.org/0052mmx10","country_code":"IN","type":"education","lineage":["https://openalex.org/I887998513"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepika Kumar","raw_affiliation_strings":["Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, Bharati Vidyapeeth's College of Engineering, New Delhi, India","institution_ids":["https://openalex.org/I887998513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101499730"],"corresponding_institution_ids":["https://openalex.org/I887998513"],"apc_list":null,"apc_paid":null,"fwci":0.8568,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85625114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"13","issue":null,"first_page":"426","last_page":"430"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9987999796867371,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9987999796867371,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9973000288009644,"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":0.9961000084877014,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.8014398813247681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7129472494125366},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6378995776176453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6352043151855469},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.5681060552597046},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4229242503643036},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3736097812652588}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8014398813247681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129472494125366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6378995776176453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6352043151855469},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.5681060552597046},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4229242503643036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3736097812652588}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iciinfs.2016.8262978","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciinfs.2016.8262978","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 11th International Conference on Industrial and Information Systems (ICIIS)","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":18,"referenced_works":["https://openalex.org/W1563088657","https://openalex.org/W1564518192","https://openalex.org/W2087347434","https://openalex.org/W2108271008","https://openalex.org/W2108807072","https://openalex.org/W2109364787","https://openalex.org/W2118726579","https://openalex.org/W2124351082","https://openalex.org/W2132641846","https://openalex.org/W2145073242","https://openalex.org/W2148603752","https://openalex.org/W2152195021","https://openalex.org/W2156909104","https://openalex.org/W2219995598","https://openalex.org/W3007818867","https://openalex.org/W6633839448","https://openalex.org/W6681651645","https://openalex.org/W6682590970"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W4376643315","https://openalex.org/W4324137541","https://openalex.org/W2900445707","https://openalex.org/W4285741730","https://openalex.org/W1191482210","https://openalex.org/W4285046548","https://openalex.org/W4210302090","https://openalex.org/W3092276832","https://openalex.org/W4375951447"],"abstract_inverted_index":{"Problems":[0],"of":[1,16,19,70,92,138],"online":[2],"learning,":[3],"or":[4],"real-world":[5],"scenarios":[6],"where":[7],"machines":[8,99],"are":[9,105],"required":[10],"to":[11,47,108,116,135],"be":[12,48],"trained":[13,95,100],"on":[14,84,101],"batches":[15,69],"data":[17,78,87],"instead":[18],"the":[20,68,75,85,89,93,98,102,110,136,139,145],"entire":[21],"superset,":[22],"necessitate":[23],"an":[24,62],"efficient":[25],"and":[26,36,79,97,141],"accurate":[27],"incremental":[28],"learning":[29,66],"approach":[30,147],"which":[31,57],"both,":[32],"consumes":[33],"less":[34],"time":[35],"is":[37,124],"computationally":[38],"light;":[39],"Incremental":[40],"Support":[41],"Vector":[42],"Machines":[43],"(SVM)":[44],"has":[45],"proved":[46],"a":[49,54,81],"promising":[50],"panacea.":[51],"We":[52],"propose":[53],"novel":[55],"approach,":[56],"firstly":[58],"uses":[59],"SVMs":[60],"in":[61],"ensemble":[63],"manner":[64],"for":[65,118],"from":[67],"data,":[71],"it":[72],"then":[73,106],"discards":[74],"correctly":[76,94],"classified":[77],"trains":[80],"new":[82],"SVM":[83],"misclassified":[86,103],"points,":[88,104],"weighted":[90],"sums":[91],"SVMs,":[96,117],"used":[107],"obtain":[109],"final":[111,119],"classification":[112,120],"values.":[113],"Weight":[114],"assignment":[115],"at":[121],"each":[122],"step,":[123],"done":[125],"using":[126],"Particle":[127],"Swam":[128],"Optimization":[129],"(PSO).":[130],"The":[131],"simulation":[132],"results":[133],"attest":[134],"veracity":[137],"model,":[140],"comparisons":[142],"suggest":[143],"that":[144],"proposed":[146],"holds":[148],"promise.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
