{"id":"https://openalex.org/W2171847105","doi":"https://doi.org/10.1109/tai.2002.1180809","title":"Error-based pruning of decision trees grown on very large data sets can work!","display_name":"Error-based pruning of decision trees grown on very large data sets can work!","publication_year":2003,"publication_date":"2003-06-26","ids":{"openalex":"https://openalex.org/W2171847105","doi":"https://doi.org/10.1109/tai.2002.1180809","mag":"2171847105"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2002.1180809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2002.1180809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","raw_type":"proceedings-article"},"type":"conference-paper","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/A5000168449","display_name":"Lawrence Hall","orcid":"https://orcid.org/0000-0002-7898-8456"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"L.O. Hall","raw_affiliation_strings":["Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100699495","display_name":"Richard Collins","orcid":"https://orcid.org/0000-0001-5449-8535"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Collins","raw_affiliation_strings":["Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019673624","display_name":"Kevin W. Bowyer","orcid":"https://orcid.org/0000-0002-7562-4390"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K.W. Bowyer","raw_affiliation_strings":["Computer Science and Engineering, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047680745","display_name":"Robert E. Banfield","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Banfield","raw_affiliation_strings":["Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, University of South Florida, Tampa, FL, USA","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.982200026512146,"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":0.982200026512146,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9817000031471252,"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/T10320","display_name":"Neural Networks and Applications","score":0.9686999917030334,"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/pruning","display_name":"Pruning","score":0.8951709866523743},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.7646416425704956},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6912398934364319},{"id":"https://openalex.org/keywords/certainty","display_name":"Certainty","score":0.557935357093811},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5521478056907654},{"id":"https://openalex.org/keywords/assertion","display_name":"Assertion","score":0.5274752974510193},{"id":"https://openalex.org/keywords/incremental-decision-tree","display_name":"Incremental decision tree","score":0.5178540349006653},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5127016305923462},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4828839600086212},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48017993569374084},{"id":"https://openalex.org/keywords/decision-tree-learning","display_name":"Decision tree learning","score":0.40216878056526184},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3853062093257904},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29257047176361084}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8951709866523743},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.7646416425704956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912398934364319},{"id":"https://openalex.org/C7493553","wikidata":"https://www.wikidata.org/wiki/Q1520777","display_name":"Certainty","level":2,"score":0.557935357093811},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5521478056907654},{"id":"https://openalex.org/C40422974","wikidata":"https://www.wikidata.org/wiki/Q741248","display_name":"Assertion","level":2,"score":0.5274752974510193},{"id":"https://openalex.org/C10229987","wikidata":"https://www.wikidata.org/wiki/Q17083028","display_name":"Incremental decision tree","level":4,"score":0.5178540349006653},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5127016305923462},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4828839600086212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48017993569374084},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.40216878056526184},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3853062093257904},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29257047176361084},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tai.2002.1180809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2002.1180809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.224.7466","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.7466","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.usf.edu/%7Erbanfiel/papers/ictai02.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.547.3141","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.547.3141","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://sci2s.ugr.es/keel/pdf/specific/congreso/Hall02EBPLargeDataSets.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.66.4924","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.4924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.csee.usf.edu/~hall/papers/ictai02.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.81.9238","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.9238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://morden.csee.usf.edu/avatar/publications/ictai02.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338291","display_name":"Sandia National Laboratories","ror":"https://ror.org/01apwpt12"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W98929787","https://openalex.org/W1504694836","https://openalex.org/W1519768305","https://openalex.org/W1598696986","https://openalex.org/W1604329830","https://openalex.org/W1648786085","https://openalex.org/W2111022379","https://openalex.org/W2125055259","https://openalex.org/W2153187042","https://openalex.org/W6604056155","https://openalex.org/W6630931566","https://openalex.org/W6635904410","https://openalex.org/W6678449394"],"related_works":["https://openalex.org/W4230916380","https://openalex.org/W2337315440","https://openalex.org/W3156253308","https://openalex.org/W4327693306","https://openalex.org/W2477932017","https://openalex.org/W2811372817","https://openalex.org/W1982169401","https://openalex.org/W2030894524","https://openalex.org/W1648970942","https://openalex.org/W2120748120"],"abstract_inverted_index":{"It":[0],"has":[1],"been":[2,46],"asserted":[3],"that,":[4,72],"using":[5,48,57],"traditional":[6],"pruning":[7,53,84],"methods,":[8],"growing":[9],"decision":[10,67,87],"trees":[11],"with":[12,97],"increasingly":[13],"larger":[14,22],"amounts":[15],"of":[16,62,78],"training":[17,99],"data":[18,38],"will":[19,85],"result":[20],"in":[21,55,64,73],"tree":[23,68,88],"sizes":[24],"even":[25],"when":[26,92],"accuracy":[27,93],"does":[28],"not":[29,95],"increase.":[30],"With":[31],"regard":[32],"to":[33,40,90],"error-based":[34,83],"pruning,":[35],"the":[36,49,58,65,79],"experimental":[37],"used":[39],"illustrate":[41],"this":[42],"assertion":[43],"have":[44],"apparently":[45],"obtained":[47],"default":[50,59],"setting":[51,77],"for":[52,82],"strength;":[54],"particular,":[56],"certainty":[60,80],"factor":[61,81],"25":[63],"C4.5":[66],"implementation.":[69],"We":[70],"show":[71],"general,":[74],"an":[75],"appropriate":[76],"cause":[86],"size":[89],"plateau":[91],"is":[94],"increasing":[96],"more":[98],"data.":[100]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
