{"id":"https://openalex.org/W2897057060","doi":"https://doi.org/10.1145/3207677.3277962","title":"Interpretable Functional Logistic Regression","display_name":"Interpretable Functional Logistic Regression","publication_year":2018,"publication_date":"2018-10-18","ids":{"openalex":"https://openalex.org/W2897057060","doi":"https://doi.org/10.1145/3207677.3277962","mag":"2897057060"},"language":"en","primary_location":{"id":"doi:10.1145/3207677.3277962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3207677.3277962","pdf_url":null,"source":{"id":"https://openalex.org/S4306523741","display_name":"Proceedings of the 2nd International Conference on Computer Science and Application Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Computer Science and Application Engineering","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/A5101288338","display_name":"Cui Lv","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cui Lv","raw_affiliation_strings":["School of Mathematics and Systems Science, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Systems Science, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022224799","display_name":"Di\u2010Rong Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119942","display_name":"Wuhan Textile University","ror":"https://ror.org/02jgsf398","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119942"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di-Rong Chen","raw_affiliation_strings":["School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, China","institution_ids":["https://openalex.org/I4210119942"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3768,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.67105263,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9900000095367432,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9900000095367432,"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/T10320","display_name":"Neural Networks and Applications","score":0.980400025844574,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9746999740600586,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7184884548187256},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5555028319358826},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.47075894474983215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4480465352535248},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.42757248878479004},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.39080411195755005},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3136484622955322},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2391851246356964}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7184884548187256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5555028319358826},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.47075894474983215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4480465352535248},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42757248878479004},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.39080411195755005},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3136484622955322},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2391851246356964}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3207677.3277962","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3207677.3277962","pdf_url":null,"source":{"id":"https://openalex.org/S4306523741","display_name":"Proceedings of the 2nd International Conference on Computer Science and Application Engineering","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd International Conference on Computer Science and Application Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W163215356","https://openalex.org/W1749992802","https://openalex.org/W1969717366","https://openalex.org/W1990385536","https://openalex.org/W2034260606","https://openalex.org/W2039470961","https://openalex.org/W2067165060","https://openalex.org/W2085811244","https://openalex.org/W2107884074","https://openalex.org/W2114200978","https://openalex.org/W2125235227","https://openalex.org/W2135046866","https://openalex.org/W2298009130","https://openalex.org/W2963596695","https://openalex.org/W2998216295","https://openalex.org/W3021971632","https://openalex.org/W3105340263","https://openalex.org/W4251011993","https://openalex.org/W4285719527","https://openalex.org/W4290960458","https://openalex.org/W4292156489","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2059099031","https://openalex.org/W4367335967","https://openalex.org/W4225160365","https://openalex.org/W2991210157","https://openalex.org/W1596769710","https://openalex.org/W1574918455","https://openalex.org/W4238660912","https://openalex.org/W2908249560","https://openalex.org/W2444264653","https://openalex.org/W2073229519"],"abstract_inverted_index":{"In":[0,65],"this":[1],"paper1":[2],"we":[3,67,90],"introduce":[4],"a":[5],"new":[6],"approach,":[7],"called":[8],"\"Interpretable":[9],"functional":[10,15],"logistic":[11],"regression\"(IFLR),":[12],"which":[13],"classifies":[14],"data":[16],"into":[17],"two":[18],"distinct":[19],"groups":[20],"and":[21,25,89,96],"provides":[22],"an":[23],"easy-to-interpret":[24],"high":[26],"predictive":[27,97],"classifier.":[28],"Our":[29],"method":[30],"is":[31,40,83],"wide":[32],"enough":[33],"to":[34],"handle":[35],"different":[36,43],"notions,":[37],"though":[38],"interpretability":[39],"comprehended":[41],"in":[42,74,85],"ways.":[44],"We":[45],"regularize":[46],"various":[47],"derivatives":[48],"of":[49,72,79],"the":[50,54,69,80,86],"coefficient":[51,55],"function,":[52],"so":[53],"function":[56],"can":[57],"be":[58],"sparse,":[59],"constant-wise,":[60],"linear-wise":[61],"or":[62],"quadratic-wise,":[63],"etc.":[64],"theory,":[66],"derive":[68],"asymptotic":[70],"rate":[71],"convergence":[73],"our":[75],"estimate.":[76],"The":[77],"usefulness":[78],"IFLR":[81],"approach":[82],"shown":[84],"numeric":[87],"world":[88],"obtain":[91],"interpretable":[92],"classifiers":[93],"with":[94],"competitive":[95],"ability.":[98]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}