{"id":"https://openalex.org/W2344041269","doi":"https://doi.org/10.5220/0005702100960104","title":"Improving Cascade Classifier Precision by Instance Selection and Outlier Generation","display_name":"Improving Cascade Classifier Precision by Instance Selection and Outlier Generation","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2344041269","doi":"https://doi.org/10.5220/0005702100960104","mag":"2344041269"},"language":"en","primary_location":{"id":"doi:10.5220/0005702100960104","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005702100960104","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0005702100960104","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064515349","display_name":"Judith Neugebauer","orcid":null},"institutions":[{"id":"https://openalex.org/I129877168","display_name":"Carl von Ossietzky Universit\u00e4t Oldenburg","ror":"https://ror.org/033n9gh91","country_code":"DE","type":"education","lineage":["https://openalex.org/I129877168"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Judith Neugebauer","raw_affiliation_strings":["Carl von Ossietzky University Oldenburg, Germany"],"affiliations":[{"raw_affiliation_string":"Carl von Ossietzky University Oldenburg, Germany","institution_ids":["https://openalex.org/I129877168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103020462","display_name":"Oliver Kr\u00e4mer","orcid":"https://orcid.org/0000-0001-7607-1700"},"institutions":[{"id":"https://openalex.org/I129877168","display_name":"Carl von Ossietzky Universit\u00e4t Oldenburg","ror":"https://ror.org/033n9gh91","country_code":"DE","type":"education","lineage":["https://openalex.org/I129877168"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Kramer","raw_affiliation_strings":["Carl von Ossietzky University Oldenburg, Germany"],"affiliations":[{"raw_affiliation_string":"Carl von Ossietzky University Oldenburg, Germany","institution_ids":["https://openalex.org/I129877168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002863938","display_name":"Michael Sonnenschein","orcid":null},"institutions":[{"id":"https://openalex.org/I129877168","display_name":"Carl von Ossietzky Universit\u00e4t Oldenburg","ror":"https://ror.org/033n9gh91","country_code":"DE","type":"education","lineage":["https://openalex.org/I129877168"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Sonnenschein","raw_affiliation_strings":["Carl von Ossietzky University Oldenburg, Germany"],"affiliations":[{"raw_affiliation_string":"Carl von Ossietzky University Oldenburg, Germany","institution_ids":["https://openalex.org/I129877168"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064515349"],"corresponding_institution_ids":["https://openalex.org/I129877168"],"apc_list":null,"apc_paid":null,"fwci":2.5709,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.91570032,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"96","last_page":"104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9909999966621399,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9909999966621399,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9871000051498413,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9819999933242798,"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/classifier","display_name":"Classifier (UML)","score":0.77134108543396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7310799360275269},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7043015956878662},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.6471673846244812},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6308488249778748},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6191558241844177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5863364338874817},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.586053729057312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.450606107711792},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.444333553314209},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.43997645378112793},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41678160429000854},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07016518712043762}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.77134108543396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7310799360275269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7043015956878662},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.6471673846244812},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6308488249778748},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6191558241844177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5863364338874817},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.586053729057312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.450606107711792},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.444333553314209},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.43997645378112793},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41678160429000854},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07016518712043762},{"id":"https://openalex.org/C42360764","wikidata":"https://www.wikidata.org/wiki/Q83588","display_name":"Chemical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5220/0005702100960104","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005702100960104","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.5220/0005702100960104","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0005702100960104","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Agents and Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2130972437","https://openalex.org/W3096423661","https://openalex.org/W2004354694","https://openalex.org/W2040550925","https://openalex.org/W2043589769","https://openalex.org/W3206106441","https://openalex.org/W2135999858","https://openalex.org/W597801233","https://openalex.org/W1964081096","https://openalex.org/W1845718094"],"abstract_inverted_index":{"Beside":[0],"the":[1,22,35,51,64,77,80,86,98,187,195,200,203],"curse":[2],"of":[3,21,63,194,202],"dimensionality":[4,20],"and":[5,30,67,89,173,179,182],"imbalanced":[6,31],"classes,":[7],"unfavorable":[8,46],"data":[9,32,47,107,157,184,189],"distributions":[10,48],"can":[11,27,44,132],"hamper":[12],"classification\r\n\r\naccuracy.":[13],"This":[14],"is":[15,34,59,116,147],"particularly":[16],"problematic":[17],"with":[18],"increasing":[19,199],"classification":[23,37,99,134],"task.\r\n\r\nA":[24],"classifier":[25,43,58,83,96,197],"that":[26,186],"handle":[28],"high-dimensional":[29],"sets":[33],"cascade":[36,42,78,196],"method\r\n\r\nfor":[38],"time":[39],"series.":[40],"The":[41,113],"compound":[45],"by":[49,198],"projecting":[50],"high-dimensional\r\n\r\ndata":[52],"set":[53,185],"onto":[54],"low-dimensional":[55,145,165],"subsets.":[56],"A":[57],"trained":[60],"for":[61,106,125,160,169],"each":[62,94],"low-dimensional\r\n\r\ndata":[65],"subsets":[66],"their":[68,142],"predictions":[69],"are":[70,140],"aggregated":[71],"to":[72,109,121,144,150],"an":[73,180],"overall":[74,87],"result.":[75],"For":[76],"classifier,":[79],"errors":[81],"of\r\n\r\neach":[82],"accumulate":[84],"in":[85,93,164],"result":[88],"therefore":[90],"small":[91,95],"improvements":[92],"can\r\n\r\nimprove":[97],"accuracy.":[100],"Therefore":[101],"we":[102],"propose":[103,154],"two":[104],"methods":[105,191],"preprocessing":[108,158,190],"improve":[110,133],"the\r\n\r\ncascade":[111],"classifier.":[112],"first":[114],"method":[115,159],"instance":[117],"selection,":[118],"a":[119,155],"technique":[120],"select":[122],"representative":[123],"examples":[124,131,139],"the\r\n\r\nclassification":[126],"task.":[127],"Furthermore,":[128],"artificial":[129,162,181],"infeasible":[130,138],"performance.":[135],"Even":[136],"if\r\n\r\nhigh-dimensional":[137],"available,":[141],"projection":[143,151],"space":[146],"not":[148],"possible\r\n\r\ndue":[149],"errors.":[152],"We":[153,167],"second":[156],"generating":[161],"infeasible\r\n\r\nexamples":[163],"space.":[166],"show":[168],"micro":[170],"Combined":[171],"Heat":[172],"Power":[174],"plant":[175],"power":[176],"production\r\n\r\ntime":[177],"series":[178],"complex":[183],"proposed":[188],"increase":[192],"the\r\n\r\nperformance":[193],"selectivity":[201],"learned":[204],"decision":[205],"boundaries.":[206]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}