{"id":"https://openalex.org/W4406699005","doi":"https://doi.org/10.48550/arxiv.2501.10667","title":"Precision Adaptive Imputation Network : An Unified Technique for Mixed Datasets","display_name":"Precision Adaptive Imputation Network : An Unified Technique for Mixed Datasets","publication_year":2025,"publication_date":"2025-01-18","ids":{"openalex":"https://openalex.org/W4406699005","doi":"https://doi.org/10.48550/arxiv.2501.10667"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2501.10667","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.10667","pdf_url":"https://arxiv.org/pdf/2501.10667","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2501.10667","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053641864","display_name":"Harsh Joshi","orcid":"https://orcid.org/0009-0000-2175-3333","observed_orcids":["https://orcid.org/0009-0000-2175-3333"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joshi, Harsh","raw_affiliation_strings":["Birla College of Arts , Science and Commerce , Kalyan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birla College of Arts , Science and Commerce , Kalyan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115980884","display_name":"Rajeshwari Mistri","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mistri, Rajeshwari","raw_affiliation_strings":["Birla College of Arts , Science and Commerce , Kalyan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birla College of Arts , Science and Commerce , Kalyan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120391801","display_name":"Manasi Mali","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mali, Manasi","raw_affiliation_strings":["Birla College of Arts , Science and Commerce , Kalyan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birla College of Arts , Science and Commerce , Kalyan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115980885","display_name":"Nachiket Kapure","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kapure, Nachiket","raw_affiliation_strings":["Birla College of Arts , Science and Commerce , Kalyan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birla College of Arts , Science and Commerce , Kalyan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112898886","display_name":"Preeti Kumari","orcid":"https://orcid.org/0009-0007-6675-6942","observed_orcids":["https://orcid.org/0009-0007-6675-6942"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumari, Parul","raw_affiliation_strings":["Birla College of Arts , Science and Commerce , Kalyan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Birla College of Arts , Science and Commerce , Kalyan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9452999830245972,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9452999830245972,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.00930000003427267,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.003599999938160181,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data-imputation","display_name":"missing data imputation","score":0.9990000128746033},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7440000176429749},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.996999979019165},{"id":"https://openalex.org/keywords/data-reconstruction","display_name":"data reconstruction","score":0.6759999990463257}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8035467863082886},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5530913472175598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4026540517807007},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3825732171535492},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3542439341545105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3144180178642273}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2501.10667","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.10667","pdf_url":"https://arxiv.org/pdf/2501.10667","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2501.10667","doi":"https://doi.org/10.48550/arxiv.2501.10667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2501.10667","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2501.10667","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.10667","pdf_url":"https://arxiv.org/pdf/2501.10667","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406699005.pdf","grobid_xml":"https://content.openalex.org/works/W4406699005.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4409762274","https://openalex.org/W4416435429","https://openalex.org/W7138050411","https://openalex.org/W7167274237","https://openalex.org/W4414126039","https://openalex.org/W4407831869","https://openalex.org/W4403582476","https://openalex.org/W2803403013","https://openalex.org/W3176020369","https://openalex.org/W4404190303"],"abstract_inverted_index":{"The":[0,109],"challenge":[1],"of":[2,16,143,170],"missing":[3,144],"data":[4,42,49,117,145,158],"remains":[5],"a":[6,36,57,140,150],"significant":[7],"obstacle":[8],"across":[9,78],"various":[10],"scientific":[11],"domains,":[12],"necessitating":[13],"the":[14,30,164],"development":[15],"advanced":[17,105],"imputation":[18,93],"techniques":[19,106],"that":[20,60],"can":[21],"effectively":[22],"address":[23],"complex":[24,125],"missingness":[25,53,128],"patterns.":[26],"This":[27,134],"study":[28],"introduces":[29],"Precision":[31],"Adaptive":[32],"Imputation":[33],"Network":[34],"(PAIN),":[35],"novel":[37],"algorithm":[38],"designed":[39],"to":[40,47,115,139],"enhance":[41],"reconstruction":[43,146],"by":[44,84],"dynamically":[45],"adapting":[46],"diverse":[48],"types,":[50],"distributions,":[51],"and":[52,66,71,86,98,119,160],"mechanisms.":[54],"PAIN":[55,89],"employs":[56],"tri-step":[58],"process":[59],"integrates":[61],"statistical":[62],"methods,":[63,94],"random":[64],"forests,":[65],"autoencoders,":[67],"ensuring":[68],"balanced":[69],"accuracy":[70],"efficiency":[72],"in":[73,124,157,173],"imputation.":[74],"Through":[75],"rigorous":[76],"evaluation":[77],"multiple":[79],"datasets,":[80],"including":[81],"those":[82],"characterized":[83],"high-dimensional":[85],"correlated":[87],"features,":[88],"consistently":[90],"outperforms":[91],"traditional":[92],"such":[95],"as":[96,101,103],"mean":[97],"median":[99],"imputation,":[100],"well":[102],"other":[104],"like":[107],"MissForest.":[108],"findings":[110],"highlight":[111],"PAIN's":[112],"superior":[113],"ability":[114],"preserve":[116],"distributions":[118],"maintain":[120],"analytical":[121],"integrity,":[122],"particularly":[123],"scenarios":[126],"where":[127],"is":[129],"not":[130,136],"completely":[131],"at":[132],"random.":[133],"research":[135],"only":[137],"contributes":[138],"deeper":[141],"understanding":[142],"but":[147],"also":[148],"provides":[149],"critical":[151],"framework":[152],"for":[153,166],"future":[154],"methodological":[155],"innovations":[156],"science":[159],"machine":[161],"learning,":[162],"paving":[163],"way":[165],"more":[167],"effective":[168],"handling":[169],"mixed-type":[171],"datasets":[172],"real-world":[174],"applications.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-01-23T00:00:00"}
