{"id":"https://openalex.org/W2725571670","doi":"https://doi.org/10.1109/icis.2017.7960061","title":"Outcome prediction of DOTA2 based on Na\u00efve Bayes classifier","display_name":"Outcome prediction of DOTA2 based on Na\u00efve Bayes classifier","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2725571670","doi":"https://doi.org/10.1109/icis.2017.7960061","mag":"2725571670"},"language":"en","primary_location":{"id":"doi:10.1109/icis.2017.7960061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7960061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-article"},"type":"article","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/A5101797385","display_name":"Kaixiang Wang","orcid":"https://orcid.org/0000-0001-9882-4726"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaixiang Wang","raw_affiliation_strings":["School of Computer Science, Communication University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014243831","display_name":"Wenqian Shang","orcid":"https://orcid.org/0009-0005-0146-1524"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqian Shang","raw_affiliation_strings":["School of Computer Science, Communication University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Communication University of China, Beijing, China","institution_ids":["https://openalex.org/I75689368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101797385"],"corresponding_institution_ids":["https://openalex.org/I75689368"],"apc_list":null,"apc_paid":null,"fwci":12.5247,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.98070576,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"591","last_page":"593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9884999990463257,"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/T11705","display_name":"Gambling Behavior and Treatments","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/3203","display_name":"Clinical Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.9140563011169434},{"id":"https://openalex.org/keywords/bayes-error-rate","display_name":"Bayes error rate","score":0.7727606892585754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7175572514533997},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7105904817581177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.700672447681427},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6725155115127563},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.6333420276641846},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4403289556503296},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4356077313423157},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34687289595603943},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22759437561035156},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15258607268333435},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.14585885405540466}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.9140563011169434},{"id":"https://openalex.org/C143809311","wikidata":"https://www.wikidata.org/wiki/Q4874458","display_name":"Bayes error rate","level":5,"score":0.7727606892585754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7175572514533997},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7105904817581177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.700672447681427},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6725155115127563},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.6333420276641846},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4403289556503296},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4356077313423157},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34687289595603943},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22759437561035156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15258607268333435},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.14585885405540466},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icis.2017.7960061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icis.2017.7960061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321160","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71"},{"id":"https://openalex.org/F4320329139","display_name":"Communication University of China","ror":"https://ror.org/04facbs33"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W328766226","https://openalex.org/W1991286329","https://openalex.org/W2573079622","https://openalex.org/W2575693274","https://openalex.org/W2747062477","https://openalex.org/W6742604928"],"related_works":["https://openalex.org/W2374047926","https://openalex.org/W2360982908","https://openalex.org/W2057359786","https://openalex.org/W2394466068","https://openalex.org/W145653800","https://openalex.org/W1986699031","https://openalex.org/W2393473353","https://openalex.org/W4312866165","https://openalex.org/W108287568","https://openalex.org/W2537862391"],"abstract_inverted_index":{"Although":[0],"DOTA2":[1,61],"is":[2,94],"a":[3,95],"popular":[4],"game":[5],"around":[6],"the":[7,18,23,26,35,42,46,50,60,66,85,100,104],"world,":[8],"no":[9],"clear":[10],"algorithm":[11],"or":[12],"software":[13],"are":[14],"designed":[15],"to":[16,49,98],"forecast":[17],"winning":[19,79],"probability":[20,80],"by":[21],"analyzing":[22],"lineups.":[24],"However,":[25],"author":[27],"finds":[28],"that":[29,90],"Naive":[30,56,73,91],"Bayes":[31,57,74,92],"classifier,":[32],"one":[33],"of":[34,77,81],"most":[36],"common":[37],"classification":[38],"algorithm,":[39],"can":[40],"analyze":[41,99],"lineups":[43,51,101],"and":[44,52,102],"predict":[45,103],"outcome":[47,105],"according":[48],"gives":[53],"an":[54],"improved":[55],"classifier.":[58],"Using":[59],"data":[62],"set":[63],"published":[64],"in":[65,84],"UCI":[67],"Machine":[68],"Learning":[69],"Repository,":[70],"we":[71],"test":[72],"classifier's":[75],"prediction":[76],"respective":[78],"both":[82],"sides":[83],"game.":[86],"The":[87],"results":[88],"show":[89],"classifier":[93],"practical":[96],"tool":[97],"based":[106],"on":[107],"players'":[108],"choices.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
