{"id":"https://openalex.org/W3036947539","doi":"https://doi.org/10.23919/date48585.2020.9116488","title":"Explainable DRC Hotspot Prediction with Random Forest and SHAP Tree Explainer","display_name":"Explainable DRC Hotspot Prediction with Random Forest and SHAP Tree Explainer","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3036947539","doi":"https://doi.org/10.23919/date48585.2020.9116488","mag":"3036947539"},"language":"en","primary_location":{"id":"doi:10.23919/date48585.2020.9116488","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","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/A5101427958","display_name":"Wei Zeng","orcid":"https://orcid.org/0000-0003-3638-1688"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Zeng","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072204999","display_name":"Azadeh Davoodi","orcid":"https://orcid.org/0000-0001-5213-2556"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Azadeh Davoodi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084170591","display_name":"Rasit Onur Topaloglu","orcid":"https://orcid.org/0000-0001-8759-6959"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rasit Onur Topaloglu","raw_affiliation_strings":["IBM Hopewell Junction, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Hopewell Junction, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"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":24,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1151","last_page":"1156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10260","display_name":"Software Engineering Research","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.8949601650238037},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.8309240341186523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7598799467086792},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5413219332695007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5027847290039062},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.445210337638855},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.428517609834671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4004223644733429},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34361958503723145}],"concepts":[{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.8949601650238037},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8309240341186523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7598799467086792},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5413219332695007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5027847290039062},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.445210337638855},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.428517609834671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4004223644733429},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34361958503723145},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/date48585.2020.9116488","is_oa":false,"landing_page_url":"https://doi.org/10.23919/date48585.2020.9116488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1675335411","https://openalex.org/W1968193130","https://openalex.org/W1976526581","https://openalex.org/W1999116995","https://openalex.org/W2161629461","https://openalex.org/W2336236367","https://openalex.org/W2554031552","https://openalex.org/W2604486584","https://openalex.org/W2624449764","https://openalex.org/W2787070805","https://openalex.org/W2805342204","https://openalex.org/W2808884553","https://openalex.org/W2899885603","https://openalex.org/W2911964244","https://openalex.org/W2962862931","https://openalex.org/W4238447347","https://openalex.org/W4295313945","https://openalex.org/W6730011401","https://openalex.org/W6737947904","https://openalex.org/W6748281036","https://openalex.org/W6752312381"],"related_works":["https://openalex.org/W4321636153","https://openalex.org/W4377964522","https://openalex.org/W2985924212","https://openalex.org/W3195610867","https://openalex.org/W4381414210","https://openalex.org/W4327511089","https://openalex.org/W4308191010","https://openalex.org/W3195168932","https://openalex.org/W4383535405","https://openalex.org/W4210974274"],"abstract_inverted_index":{"With":[0],"advanced":[1],"technology":[2],"nodes,":[3],"resolving":[4],"design":[5,27],"rule":[6],"check":[7],"(DRC)":[8],"violations":[9],"has":[10],"become":[11],"a":[12,63,81],"cumbersome":[13],"task,":[14],"which":[15,130],"makes":[16,131],"it":[17,132],"desirable":[18],"to":[19,68,79],"make":[20,87],"predictions":[21,96],"at":[22,48],"earlier":[23],"stages":[24],"of":[25,65],"the":[26,35,44,49,66,70,76],"flow.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32],"show":[33,100],"that":[34,101],"Random":[36],"Forest":[37],"(RF)":[38],"model":[39],"is":[40,103],"quite":[41],"effective":[42],"for":[43,75,92,134],"DRC":[45,94,135],"hotspot":[46,95,136],"prediction":[47],"global":[50],"routing":[51],"stage,":[52],"and":[53,89,123],"in":[54,106,118],"fact":[55],"significantly":[56],"outperforms":[57],"recent":[58,82],"prior":[59],"works,":[60],"with":[61,112],"only":[62],"fraction":[64],"runtime":[67],"develop":[69],"model.":[71],"We":[72],"also":[73],"propose,":[74],"first":[77],"time,":[78],"adopt":[80],"explanatory":[83],"metric-the":[84],"SHAP":[85],"value-to":[86],"accurate":[88],"consistent":[90],"explanations":[91],"individual":[93],"from":[97],"RF.":[98],"Experiments":[99],"RF":[102],"21%-60%":[104],"better":[105],"predictive":[107],"performance":[108],"on":[109],"average,":[110],"compared":[111],"promising":[113],"machine":[114],"learning":[115],"models":[116],"used":[117],"similar":[119],"works":[120],"(e.g.":[121],"SVM":[122],"neural":[124],"networks)":[125],"while":[126],"exhibiting":[127],"good":[128],"explainability,":[129],"ideal":[133],"prediction.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
