{"id":"https://openalex.org/W7147082880","doi":"https://doi.org/10.1109/cnml68938.2026.11452411","title":"Two-layer Hybrid XGBoost based on Sample Nearest Neighbor Enveloping Transposition Projection Mechanism","display_name":"Two-layer Hybrid XGBoost based on Sample Nearest Neighbor Enveloping Transposition Projection Mechanism","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147082880","doi":"https://doi.org/10.1109/cnml68938.2026.11452411"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452411","is_oa":true,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452411","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":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/cnml68938.2026.11452411","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132703219","display_name":"Zhixuan Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhixuan Fan","raw_affiliation_strings":["Chongqing University,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"Chongqing University,Chongqing,China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132694762","display_name":"Jie Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Ma","raw_affiliation_strings":["Chongqing University,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"Chongqing University,Chongqing,China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090925242","display_name":"Tao Zhou","orcid":"https://orcid.org/0000-0003-0561-2316"},"institutions":[{"id":"https://openalex.org/I4210107284","display_name":"Chongqing Academy of Science and Technology","ror":"https://ror.org/01gnzp994","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210107284"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Zhou","raw_affiliation_strings":["Chongqing Academy of Information and Communications Technology,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"Chongqing Academy of Information and Communications Technology,Chongqing,China","institution_ids":["https://openalex.org/I4210107284"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128704933","display_name":"Xiaoping Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Zeng","raw_affiliation_strings":["Chongqing University,Chongqing,China"],"affiliations":[{"raw_affiliation_string":"Chongqing University,Chongqing,China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5132703219"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88475694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1012","last_page":"1016"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.39399999380111694,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.39399999380111694,"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/T10057","display_name":"Face and Expression Recognition","score":0.14259999990463257,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09350000321865082,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/sample","display_name":"Sample (material)","score":0.6967999935150146},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6381000280380249},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6035000085830688},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5637999773025513},{"id":"https://openalex.org/keywords/transposition","display_name":"Transposition (logic)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/envelope","display_name":"Envelope (radar)","score":0.4551999866962433},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.38940000534057617}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6967999935150146},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6381000280380249},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6035000085830688},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5618000030517578},{"id":"https://openalex.org/C12455157","wikidata":"https://www.wikidata.org/wiki/Q7835331","display_name":"Transposition (logic)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C65155139","wikidata":"https://www.wikidata.org/wiki/Q5380912","display_name":"Envelope (radar)","level":3,"score":0.4551999866962433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.454800009727478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38999998569488525},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.375900000333786},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C184879717","wikidata":"https://www.wikidata.org/wiki/Q1374523","display_name":"Nearest neighbour algorithm","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C3020318244","wikidata":"https://www.wikidata.org/wiki/Q4812187","display_name":"Large sample","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C100279318","wikidata":"https://www.wikidata.org/wiki/Q467440","display_name":"Sample space","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452411","is_oa":true,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452411","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":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/cnml68938.2026.11452411","is_oa":true,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452411","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":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2095848001","https://openalex.org/W2295598076","https://openalex.org/W2908436867","https://openalex.org/W3206537722","https://openalex.org/W4212905376","https://openalex.org/W4221140034","https://openalex.org/W4283520892","https://openalex.org/W4283715676","https://openalex.org/W4308112567","https://openalex.org/W4381383350","https://openalex.org/W4389513437","https://openalex.org/W4392877897","https://openalex.org/W4396605083","https://openalex.org/W4404843705"],"related_works":[],"abstract_inverted_index":{"XGBoost":[0,74,109,158],"is":[1,95],"a":[2,44,64,92,100],"widely":[3],"recognized":[4],"and":[5,70,97,116,120,149],"efficient":[6],"ensemble":[7],"learning":[8],"algorithm.":[9],"However,":[10],"existing":[11],"methods":[12],"commonly":[13],"train":[14,69],"classifiers":[15],"directly":[16],"on":[17,51,88,113,126],"the":[18,24,52,77,114,145],"original":[19,115,146],"samples":[20,80],"without":[21],"effectively":[22],"considering":[23],"correlation":[25],"information":[26],"between":[27],"them,":[28],"leading":[29],"to":[30,68,142,156],"limited":[31],"accuracy":[32],"in":[33],"cases":[34],"of":[35,81,139,153],"low":[36],"sample":[37,66,84,93,103,147],"separability.":[38],"To":[39],"address":[40],"this,":[41],"we":[42],"propose":[43],"novel":[45],"\u2019two-layer":[46],"hybrid":[47],"XG-Boost\u2019":[48],"model":[49,62],"based":[50,87],"Sample":[53],"Nearest":[54],"Neighbor":[55],"Enveloping":[56],"Transposition":[57],"Projection":[58],"Mechanism":[59],"(SNNEPM).":[60],"This":[61],"constructs":[63],"two-layer":[65],"space":[67],"fuse":[71],"two":[72,108],"separate":[73],"models.":[75],"First,":[76],"nearest":[78],"neighbor":[79],"each":[82],"current":[83],"are":[85,111,123],"calculated":[86],"manifold":[89],"distance.":[90],"Second,":[91],"envelope":[94,102,117],"constructed":[96],"reconstructed":[98],"into":[99],"new":[101],"via":[104],"transposition":[105],"projection.":[106],"Finally,":[107],"models":[110],"trained":[112],"samples,":[118],"respectively,":[119],"their":[121],"predictions":[122],"fused.":[124],"Experiments":[125],"10":[127],"public":[128],"datasets":[129],"show":[130],"that":[131],"our":[132],"method":[133],"improves":[134],"ACC":[135],"by":[136,150],"an":[137,151],"average":[138,152],"2%":[140],"compared":[141,155],"using":[143],"only":[144],"space,":[148],"1.54%":[154],"state-of-the-art":[157],"variants,":[159],"demonstrating":[160],"its":[161],"effectiveness.":[162]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}