{"id":"https://openalex.org/W2913542416","doi":"https://doi.org/10.1109/bigdata.2018.8622390","title":"Heuristics Significance of Neuro-Ensemble-based Time Series Classification","display_name":"Heuristics Significance of Neuro-Ensemble-based Time Series Classification","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913542416","doi":"https://doi.org/10.1109/bigdata.2018.8622390","mag":"2913542416"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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/A5010973892","display_name":"Souka\u00efna Filali Boubrahimi","orcid":"https://orcid.org/0000-0001-5693-6383"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soukaina Filali Boubrahimi","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009847987","display_name":"Rafal A. Angryk","orcid":"https://orcid.org/0000-0001-9598-8207"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rafal Angryk","raw_affiliation_strings":["Department of Computer Science, Georgia State University, Atlanta, GA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia State University, Atlanta, GA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":0.1804,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43273054,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"35","issue":null,"first_page":"6","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9921000003814697,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7843368053436279},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.7254536151885986},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.7245612740516663},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7244704961776733},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6799325346946716},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5862593650817871},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5665457844734192},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5640356540679932},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.412531316280365},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.41018420457839966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40370115637779236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33406904339790344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7843368053436279},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7254536151885986},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.7245612740516663},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7244704961776733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6799325346946716},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5862593650817871},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5665457844734192},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5640356540679932},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.412531316280365},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.41018420457839966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40370115637779236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33406904339790344},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2018.8622390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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":44,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W116375701","https://openalex.org/W273955616","https://openalex.org/W1484746829","https://openalex.org/W1503705371","https://openalex.org/W1519451279","https://openalex.org/W1534304300","https://openalex.org/W1563375353","https://openalex.org/W1597504361","https://openalex.org/W2008348094","https://openalex.org/W2023294425","https://openalex.org/W2038181716","https://openalex.org/W2039260438","https://openalex.org/W2091921805","https://openalex.org/W2098154993","https://openalex.org/W2108400301","https://openalex.org/W2118529802","https://openalex.org/W2143325592","https://openalex.org/W2147880780","https://openalex.org/W2152761983","https://openalex.org/W2166547175","https://openalex.org/W2167467747","https://openalex.org/W2170238273","https://openalex.org/W2540083950","https://openalex.org/W2585090940","https://openalex.org/W2783151135","https://openalex.org/W2783809466","https://openalex.org/W2799718654","https://openalex.org/W2802469157","https://openalex.org/W2885560004","https://openalex.org/W2902154084","https://openalex.org/W2912528874","https://openalex.org/W2954016381","https://openalex.org/W2963069035","https://openalex.org/W4241186228","https://openalex.org/W6610017368","https://openalex.org/W6628898536","https://openalex.org/W6630094177","https://openalex.org/W6631142890","https://openalex.org/W6681479321","https://openalex.org/W6732967241","https://openalex.org/W6753628294","https://openalex.org/W6758461380","https://openalex.org/W6764971930"],"related_works":["https://openalex.org/W1981866886","https://openalex.org/W2052615004","https://openalex.org/W2099182244","https://openalex.org/W2794896638","https://openalex.org/W2046975922","https://openalex.org/W1580150424","https://openalex.org/W2891633941","https://openalex.org/W2325482571","https://openalex.org/W4361733484","https://openalex.org/W4390905871"],"abstract_inverted_index":{"Ensemble":[0],"learning":[1,23],"is":[2,130],"a":[3,36,42],"popular":[4],"paradigm":[5],"for":[6],"improving":[7],"the":[8,19,52,58,62,103,110,123],"predictive":[9],"performance":[10],"of":[11,21,54,112,125],"individual":[12],"classifiers.":[13],"In":[14],"this":[15],"work,":[16],"we":[17,85],"approach":[18,114],"problem":[20],"ensemble":[22,59,90],"from":[24,102],"an":[25],"optimization":[26],"perspective":[27],"applied":[28],"on":[29,41,67,96],"time":[30,99],"series":[31,100],"data.":[32],"We":[33,71,92],"propose":[34],"Neuro-Ensemble,":[35],"classifier":[37,56],"fusion":[38],"model":[39,129],"based":[40,66],"shallow":[43],"Multi-Layer":[44],"Perceptron":[45],"(MLP)":[46],"meta-learner.":[47],"The":[48],"neural":[49],"network":[50],"learns":[51],"expertise":[53,69],"each":[55],"in":[57],"and":[60,73,82,119,121],"optimizes":[61],"entire":[63],"classification":[64],"schema":[65],"class-level":[68],"weights.":[70],"defined":[72],"compared":[74],"three":[75],"different":[76],"classifiers":[77],"ordering":[78],"heuristics:":[79],"Random,":[80],"BestFirst,":[81],"BestLast,":[83],"that":[84,122],"coupled":[86],"with":[87,115,127],"our":[88,94,113],"new":[89],"technique.":[91],"validated":[93],"Neuro-Ensemble":[95,128],"43":[97],"real-world":[98],"datasets":[101],"UCR":[104],"repository.":[105],"Our":[106],"experimental":[107],"results":[108],"shows":[109],"competitiveness":[111],"respect":[116],"to":[117],"Evaluation":[118],"Selection":[120],"use":[124],"heuristics":[126],"insignificant.":[131]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}