{"id":"https://openalex.org/W7155645678","doi":"https://doi.org/10.1145/3787279.3788497","title":"An Adaptive Weighted Deep Forest Classification Model based on WKNN","display_name":"An Adaptive Weighted Deep Forest Classification Model based on WKNN","publication_year":2025,"publication_date":"2025-11-14","ids":{"openalex":"https://openalex.org/W7155645678","doi":"https://doi.org/10.1145/3787279.3788497"},"language":null,"primary_location":{"id":"doi:10.1145/3787279.3788497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787279.3788497","pdf_url":null,"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 2025 9th International Conference on Advances in Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3787279.3788497","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101736347","display_name":"Tianyuan Chen","orcid":"https://orcid.org/0000-0002-2947-5208"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Minnan Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"chen tianyuan","raw_affiliation_strings":["office, library of minnan normal univ, Zhangzhou, Fujian, China"],"raw_orcid":"https://orcid.org/0000-0002-4966-9843","affiliations":[{"raw_affiliation_string":"office, library of minnan normal univ, Zhangzhou, Fujian, China","institution_ids":["https://openalex.org/I9356336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134590657","display_name":"mei xin","orcid":"https://orcid.org/0000-0002-7160-929X"},"institutions":[{"id":"https://openalex.org/I9356336","display_name":"Minnan Normal University","ror":"https://ror.org/02vj1vm13","country_code":"CN","type":"education","lineage":["https://openalex.org/I9356336"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"mei xin","raw_affiliation_strings":["computer science college of minnan normal univ, zhangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-7160-929X","affiliations":[{"raw_affiliation_string":"computer science college of minnan normal univ, zhangzhou, China","institution_ids":["https://openalex.org/I9356336"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101736347"],"corresponding_institution_ids":["https://openalex.org/I9356336"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.77621301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"53","last_page":"60"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13748","display_name":"Advanced Statistical Modeling Techniques","score":0.07490000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T13748","display_name":"Advanced Statistical Modeling Techniques","score":0.07490000128746033,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.061500001698732376,"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/T12676","display_name":"Machine Learning and ELM","score":0.04280000180006027,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5364999771118164},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.49889999628067017},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4943000078201294},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4893999993801117},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.47620001435279846},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41940000653266907},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.40299999713897705},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.39070001244544983},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35530000925064087}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6324999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6197999715805054},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5364999771118164},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4893999993801117},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.47620001435279846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43479999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39899998903274536},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.39070001244544983},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C162040801","wikidata":"https://www.wikidata.org/wiki/Q799897","display_name":"Bootstrap aggregating","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.31470000743865967},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3787279.3788497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787279.3788497","pdf_url":null,"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 2025 9th International Conference on Advances in Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3787279.3788497","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3787279.3788497","pdf_url":null,"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 2025 9th International Conference on Advances in Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5399019122123718,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2122111042","https://openalex.org/W2608021859","https://openalex.org/W2803745890","https://openalex.org/W2886710006","https://openalex.org/W2911854833","https://openalex.org/W2913690321","https://openalex.org/W2913777178","https://openalex.org/W2920660102","https://openalex.org/W2998204190","https://openalex.org/W3008550610","https://openalex.org/W3008813947","https://openalex.org/W3013221435","https://openalex.org/W3031670359","https://openalex.org/W3035460133","https://openalex.org/W3042595622","https://openalex.org/W3127995524","https://openalex.org/W3128287246","https://openalex.org/W3165980973","https://openalex.org/W3202275048","https://openalex.org/W4220859562","https://openalex.org/W4226523400","https://openalex.org/W4232714830","https://openalex.org/W4280581663","https://openalex.org/W4285059849","https://openalex.org/W4285090507","https://openalex.org/W4285255983","https://openalex.org/W4286208823","https://openalex.org/W4288070315","https://openalex.org/W4296900394","https://openalex.org/W4297906923","https://openalex.org/W4303980688","https://openalex.org/W4319659943","https://openalex.org/W4365515317","https://openalex.org/W4365517998","https://openalex.org/W4378389698"],"related_works":[],"abstract_inverted_index":{"An":[0],"improved":[1],"adaptive":[2],"weighted":[3],"deep":[4,28,194],"forest":[5,51,56,88,158,195],"model":[6,35,52,57,89,99,159,170],"is":[7,129],"proposed":[8],"in":[9,20,58],"this":[10,169],"paper,":[11],"which":[12],"improves":[13],"the":[14,21,31,34,37,42,46,55,59,62,71,76,84,87,91,95,98,102,107,113,118,122,126,133,139,144,154,157,161,179,193],"classification":[15,176],"performance":[16],"by":[17,53,116],"introducing":[18],"weights":[19],"cascade":[22],"training":[23,32,47,54,67,103,114,135],"and":[24,41,74,105,109,142,147,187,191],"prediction":[25,96,140,162],"stage":[26],"of":[27,45,80,86,112,150,156],"forest.":[29],"In":[30,94],"phase,":[33],"learns":[36],"feature":[38],"importance":[39],"values":[40,111,120],"confidence":[43,78],"score":[44,79,149],"samples":[48,68,73,82,115,136,141,152],"on":[49,90,160,178],"each":[50],"cascade.":[60],"Then,":[61,125],"WKNN":[63,127],"algorithm":[64,128],"selects":[65],"K1":[66],"closest":[69,137],"to":[70,131,138],"validation":[72,92],"calculates":[75],"average":[77,145],"these":[81,151],"as":[83,153],"weight":[85,155],"samples.":[93,163],"stage,":[97],"first":[100],"predicts":[101],"set":[104],"obtains":[106],"correct":[108,146],"wrong":[110],"comparing":[117],"predicted":[119],"with":[121],"actual":[123],"values.":[124],"used":[130],"find":[132],"K2":[134],"calculate":[143],"error":[148],"The":[164],"experimental":[165],"results":[166],"show":[167],"that":[168],"outperforms":[171],"other":[172],"mainstream":[173],"machine":[174],"learning":[175],"models":[177],"UCI":[180],"data":[181],"set,":[182],"providing":[183],"a":[184],"new":[185],"idea":[186],"method":[188],"for":[189],"improving":[190],"applying":[192],"algorithm.":[196]},"counts_by_year":[],"updated_date":"2026-04-26T06:07:20.044499","created_date":"2026-04-26T00:00:00"}
