{"id":"https://openalex.org/W4310882134","doi":"https://doi.org/10.1145/3563357.3566147","title":"Trimming outliers using trees","display_name":"Trimming outliers using trees","publication_year":2022,"publication_date":"2022-11-09","ids":{"openalex":"https://openalex.org/W4310882134","doi":"https://doi.org/10.1145/3563357.3566147"},"language":"en","primary_location":{"id":"doi:10.1145/3563357.3566147","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563357.3566147","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563357.3566147","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3563357.3566147","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071874699","display_name":"Chun Fu","orcid":"https://orcid.org/0000-0002-6152-3257"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chun Fu","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040213611","display_name":"Pandarasamy Arjunan","orcid":"https://orcid.org/0000-0002-7697-3576"},"institutions":[{"id":"https://openalex.org/I4210167254","display_name":"Singapore-MIT Alliance for Research and Technology","ror":"https://ror.org/05yb3w112","country_code":"SG","type":"education","lineage":["https://openalex.org/I4210167254"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Pandarasamy Arjunan","raw_affiliation_strings":["Berkeley Education Alliance for Research in Singapore Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Berkeley Education Alliance for Research in Singapore Limited","institution_ids":["https://openalex.org/I4210167254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045303713","display_name":"Clayton Miller","orcid":"https://orcid.org/0000-0002-1186-4299"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Clayton Miller","raw_affiliation_strings":["National University of Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8324,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.78223655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"456","last_page":"461"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.994700014591217,"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":0.994700014591217,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7233924865722656},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6612156629562378},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6603432893753052},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.649718165397644},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6294069886207581},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5723768472671509},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5710911750793457},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5028919577598572},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5025382041931152},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4855939745903015},{"id":"https://openalex.org/keywords/trimming","display_name":"Trimming","score":0.480313241481781},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.32707107067108154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7233924865722656},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6612156629562378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6603432893753052},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.649718165397644},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6294069886207581},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5723768472671509},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5710911750793457},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5028919577598572},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5025382041931152},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4855939745903015},{"id":"https://openalex.org/C56951928","wikidata":"https://www.wikidata.org/wiki/Q3539213","display_name":"Trimming","level":2,"score":0.480313241481781},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32707107067108154},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3563357.3566147","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563357.3566147","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563357.3566147","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},{"id":"pmh:oai:scholarbank.nus.edu.sg:10635/236539","is_oa":false,"landing_page_url":"https://scholarbank.nus.edu.sg/handle/10635/236539","pdf_url":null,"source":{"id":"https://openalex.org/S7407052290","display_name":"National University of Singapore","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Elements","raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.1145/3563357.3566147","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563357.3566147","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563357.3566147","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"},{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4310882134.pdf","grobid_xml":"https://content.openalex.org/works/W4310882134.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2050299018","https://openalex.org/W2089662726","https://openalex.org/W2416018206","https://openalex.org/W2521479021","https://openalex.org/W2529740945","https://openalex.org/W2786918196","https://openalex.org/W2794620754","https://openalex.org/W2965309216","https://openalex.org/W3011430044","https://openalex.org/W3043193240","https://openalex.org/W3080957353","https://openalex.org/W3102477513","https://openalex.org/W4226114411"],"related_works":["https://openalex.org/W4238897586","https://openalex.org/W435179959","https://openalex.org/W2619091065","https://openalex.org/W2059640416","https://openalex.org/W3209254368","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W3107369729"],"abstract_inverted_index":{"Prediction":[0],"of":[1,14,21,37,48,117,153,183,195,211],"building":[2],"energy":[3,42,100,212],"consumption":[4],"using":[5],"machine":[6],"learning":[7,131,210],"models":[8,39],"has":[9],"been":[10,29,66],"a":[11,51,73,91,111,128,151,187,203],"focal":[12],"point":[13],"research":[15,207],"for":[16,83,93,157,205],"decades.":[17],"However,":[18],"some":[19],"causes":[20],"forecast":[22],"errors,":[23],"particularly":[24,188],"data":[25,49],"quality,":[26,50],"have":[27,65,186],"not":[28],"adequately":[30],"addressed,":[31],"which":[32],"may":[33],"affect":[34],"the":[35,46,61,78,159,168,181,192,198],"accuracy":[36,194],"forecasting":[38],"and":[40,147,171],"subsequent":[41],"management.":[43],"To":[44],"solve":[45],"issue":[47],"classifier":[52,133],"that":[53,63,178],"can":[54,179,200],"automatically":[55],"detect":[56],"time":[57,101,121],"series":[58,102],"anomalies":[59,106,116],"is":[60,127],"goal":[62],"researchers":[64],"pursuing.":[67],"Large-scale":[68],"Energy":[69],"Anomaly":[70],"Detection":[71],"(LEAD),":[72],"community":[74],"competition":[75,199],"hosted":[76],"on":[77,141,208],"Kaggle":[79],"platform,":[80],"was":[81],"created":[82],"this":[84,97],"purpose":[85],"as":[86,88,137,139,202],"well":[87],"to":[89,109,114],"provide":[90],"foundation":[92],"benchmarking":[94],"solutions.":[95],"In":[96],"competition,":[98],"200":[99],"worldwide":[103],"with":[104,134],"labeled":[105],"were":[107],"provided":[108],"train":[110],"classification":[112,160,193],"model":[113],"predict":[115],"another":[118],"206":[119],"unseen":[120],"series.":[122],"The":[123],"proposed":[124],"winning":[125],"solution":[126],"tree-based":[129],"supervised":[130,209],"anomaly":[132,213],"ROC-AUC":[135],"score":[136],"high":[138],"0.9866":[140],"private":[142],"leaderboard.":[143],"This":[144],"article":[145],"describes":[146],"analyzes":[148],"in":[149,197],"depth":[150],"variety":[152],"commonly":[154],"employed":[155],"techniques":[156],"improving":[158],"model.":[161],"Among":[162],"these":[163],"strategies,":[164],"feature":[165],"engineering":[166],"requires":[167],"most":[169],"effort":[170],"dominates":[172],"all":[173],"other":[174],"techniques;":[175],"value-changing":[176],"features":[177],"represent":[180],"level":[182],"time-series":[184],"variation":[185],"positive":[189],"impact.":[190],"Besides,":[191],"solutions":[196],"serve":[201],"benchmark":[204],"future":[206],"detection.":[214]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2022-12-20T00:00:00"}