{"id":"https://openalex.org/W3048248109","doi":"https://doi.org/10.1145/3370748.3406566","title":"How to cultivate a green decision tree without loss of accuracy?","display_name":"How to cultivate a green decision tree without loss of accuracy?","publication_year":2020,"publication_date":"2020-08-07","ids":{"openalex":"https://openalex.org/W3048248109","doi":"https://doi.org/10.1145/3370748.3406566","mag":"3048248109"},"language":"en","primary_location":{"id":"doi:10.1145/3370748.3406566","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3370748.3406566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design","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/A5035796957","display_name":"Tseng\u2010Yi Chen","orcid":"https://orcid.org/0000-0003-2939-2821"},"institutions":[{"id":"https://openalex.org/I22265921","display_name":"National Central University","ror":"https://ror.org/00944ve71","country_code":"TW","type":"education","lineage":["https://openalex.org/I22265921"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tseng-Yi Chen","raw_affiliation_strings":["National Central University, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Central University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I22265921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073534245","display_name":"Yuan-Hao Chang","orcid":"https://orcid.org/0000-0002-1282-2111"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yuan-Hao Chang","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102930563","display_name":"Ming-Chang Yang","orcid":"https://orcid.org/0000-0002-4029-757X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Ming-Chang Yang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068313713","display_name":"Huangwei Chen","orcid":"https://orcid.org/0009-0004-7088-4590"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Huang-Wei Chen","raw_affiliation_strings":["Yuan Ze University, Taoyuan, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9997000098228455,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9997000098228455,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/computer-science","display_name":"Computer science","score":0.7574740648269653},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.742566704750061},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6846188902854919},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6713377833366394},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6376471519470215},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.6008337736129761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5065242052078247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4799409508705139},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12140259146690369},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0995919406414032}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7574740648269653},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.742566704750061},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6846188902854919},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6713377833366394},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6376471519470215},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6008337736129761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5065242052078247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4799409508705139},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12140259146690369},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0995919406414032},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3370748.3406566","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3370748.3406566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8899999856948853,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W85229681","https://openalex.org/W584952072","https://openalex.org/W1972388231","https://openalex.org/W2085988980","https://openalex.org/W2111022379","https://openalex.org/W2111746072","https://openalex.org/W2128420091","https://openalex.org/W2145073242","https://openalex.org/W2157395790","https://openalex.org/W2207893099","https://openalex.org/W2343841005","https://openalex.org/W2911964244","https://openalex.org/W2955033349","https://openalex.org/W3102027041","https://openalex.org/W4245341880","https://openalex.org/W4250722633","https://openalex.org/W4297957988","https://openalex.org/W6681651645"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W1585770001","https://openalex.org/W2075873371","https://openalex.org/W4245210885","https://openalex.org/W84383711","https://openalex.org/W4242351093","https://openalex.org/W1566212037","https://openalex.org/W4285312748","https://openalex.org/W2157743338","https://openalex.org/W3203767529"],"abstract_inverted_index":{"Decision":[0],"tree":[1,31,37,49,136,139],"is":[2,42,87],"the":[3,7,22,40,57,75,79,160,164],"core":[4],"algorithm":[5,32],"of":[6,59,78,170],"random":[8],"forest":[9],"learning":[10,24],"that":[11],"has":[12],"been":[13,71],"widely":[14],"applied":[15],"to":[16,73,131],"classification":[17],"and":[18,105,117],"regression":[19],"problems":[20],"in":[21,52],"machine":[23],"field.":[25],"For":[26],"avoiding":[27],"underfitting,":[28],"a":[29,43,47,60,65,85,133,138,149],"decision":[30,61,81,135],"will":[33,50,111],"stop":[34],"growing":[35],"its":[36],"model":[38,41,76,140],"when":[39],"fully-grown":[44,48,80],"tree.":[45,62,82],"However,":[46],"result":[51],"an":[53,91],"overfitting":[54],"problem":[55],"reducing":[56],"accuracy":[58],"In":[63,129],"such":[64,84],"dilemma,":[66],"some":[67],"post-pruning":[68],"strategies":[69],"have":[70,98],"proposed":[72],"reduce":[74],"complexity":[77],"Nevertheless,":[83],"process":[86],"very":[88],"energy-inefficiency":[89],"over":[90],"non-volatile-memory-based":[92],"(NVM-based)":[93],"system":[94,167],"because":[95],"NVM":[96],"generally":[97],"high":[99,113],"writing":[100,114],"costs":[101],"(i.e.,":[102,137],"energy":[103,115,144,161],"consumption":[104,116,162],"I/O":[106,119],"latency).":[107],"Such":[108],"unnecessary":[109],"data":[110],"induce":[112],"long":[118],"latency":[120],"on":[121,163],"NVM-based":[122,165],"architectures,":[123],"especially":[124],"for":[125],"low-power-oriented":[126],"embedded":[127],"systems.":[128],"order":[130],"establish":[132],"green":[134],"with":[141],"minimized":[142],"construction":[143],"consumption),":[145],"this":[146],"study":[147],"rethinks":[148],"pruning":[150,154],"algorithm,":[151],"namely":[152],"duo-phase":[153],"framework,":[155],"which":[156],"can":[157],"significantly":[158],"decrease":[159],"computing":[166],"without":[168],"loss":[169],"accuracy.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
