{"id":"https://openalex.org/W2084959893","doi":"https://doi.org/10.1145/312129.312275","title":"Mining features for sequence classification","display_name":"Mining features for sequence classification","publication_year":1999,"publication_date":"1999-08-01","ids":{"openalex":"https://openalex.org/W2084959893","doi":"https://doi.org/10.1145/312129.312275","mag":"2084959893"},"language":"en","primary_location":{"id":"doi:10.1145/312129.312275","doi":"https://doi.org/10.1145/312129.312275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/312129.312275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/312129.312275","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 fifth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/312129.312275","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015301843","display_name":"Neal B. Lesh","orcid":"https://orcid.org/0000-0003-0027-3247","observed_orcids":["https://orcid.org/0000-0003-0027-3247"]},"institutions":[{"id":"https://openalex.org/I4405253225","display_name":"Mitsubishi Electric Research Laboratories (United States)","ror":"https://ror.org/00e2e3n51","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4405253225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neal Lesh","raw_affiliation_strings":["MERL - Mitsubishi Electric Research Laboratory, 201 Broadway, 8th Floor, Cambridge, MA","MERL - Mitsubishi Electric Research Laboratory, 201 Broadway, 8th Floor, Cambridge, MA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MERL - Mitsubishi Electric Research Laboratory, 201 Broadway, 8th Floor, Cambridge, MA","institution_ids":["https://openalex.org/I4405253225"]},{"raw_affiliation_string":"MERL - Mitsubishi Electric Research Laboratory, 201 Broadway, 8th Floor, Cambridge, MA#TAB#","institution_ids":["https://openalex.org/I4405253225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019559411","display_name":"Mohammed Javeed Zaki","orcid":"https://orcid.org/0000-0003-4711-0234","observed_orcids":["https://orcid.org/0000-0003-4711-0234"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammed J. Zaki","raw_affiliation_strings":["Computer Science Dept., Rensselaer Polytechnic Institute, Troy, NY","Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Dept., Rensselaer Polytechnic Institute, Troy, NY","institution_ids":["https://openalex.org/I165799507"]},{"raw_affiliation_string":"Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019383096","display_name":"Mitsunori Ogihara","orcid":"https://orcid.org/0000-0002-5690-7854","observed_orcids":["https://orcid.org/0000-0002-5690-7854"]},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mitsunori Ogihara","raw_affiliation_strings":["Computer Science Dept., U. of Rochester, Rochester, NY","Computer Science Dept., U. of Rochester, Rochester, NY#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Dept., U. of Rochester, Rochester, NY","institution_ids":["https://openalex.org/I5388228"]},{"raw_affiliation_string":"Computer Science Dept., U. of Rochester, Rochester, NY#TAB#","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3447,"has_fulltext":true,"cited_by_count":164,"citation_normalized_percentile":{"value":0.92229965,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"342","last_page":"346"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9648000001907349,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9648000001907349,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.021299999207258224,"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":0.006300000008195639,"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/feature-selection","display_name":"feature selection","score":0.8560000061988831},{"id":"https://openalex.org/keywords/sequential-pattern-mining","display_name":"sequential pattern mining","score":0.7929999828338623},{"id":"https://openalex.org/keywords/sequence-classification","display_name":"sequence classification","score":0.871999979019165},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.5730000138282776},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.8799999952316284},{"id":"https://openalex.org/keywords/sequential-data","display_name":"sequential data","score":0.34599998593330383}],"concepts":[{"id":"https://openalex.org/C2778805511","wikidata":"https://www.wikidata.org/wiki/Q1713","display_name":"Citation","level":2,"score":0.7363983988761902},{"id":"https://openalex.org/C161191863","wikidata":"https://www.wikidata.org/wiki/Q199655","display_name":"Library science","level":1,"score":0.6057068109512329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5277087092399597},{"id":"https://openalex.org/C2777819327","wikidata":"https://www.wikidata.org/wiki/Q5009918","display_name":"DEPT","level":2,"score":0.5127881169319153},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.39797109365463257},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3293401002883911},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31525447964668274},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10183250904083252},{"id":"https://openalex.org/C71240020","wikidata":"https://www.wikidata.org/wiki/Q186011","display_name":"Stereochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1145/312129.312275","doi":"https://doi.org/10.1145/312129.312275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/312129.312275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/312129.312275","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 fifth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.10.1899","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.1899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.merl.com/papers/docs/TR1998-22.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.150.5272","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.5272","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/~zaki/PaperDir/KDD99.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.33.3083","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.3083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rochester.edu/u/ogihara/research/KDD/features.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.43.9083","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.9083","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/~zaki/PS/KDD99.ps.gz","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/312129.312275","doi":"https://doi.org/10.1145/312129.312275","is_oa":true,"landing_page_url":"https://doi.org/10.1145/312129.312275","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/312129.312275","source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 fifth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2084959893.pdf","grobid_xml":"https://content.openalex.org/works/W2084959893.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W60969841","https://openalex.org/W187660804","https://openalex.org/W1496929357","https://openalex.org/W1507029541","https://openalex.org/W1553244859","https://openalex.org/W1564048035","https://openalex.org/W1632767318","https://openalex.org/W2007780422","https://openalex.org/W2054726834","https://openalex.org/W2083140545","https://openalex.org/W2129113961","https://openalex.org/W2140894515","https://openalex.org/W2143732008","https://openalex.org/W2146348450","https://openalex.org/W2150147654","https://openalex.org/W2154642793","https://openalex.org/W2158454296","https://openalex.org/W2167659919","https://openalex.org/W2800394774","https://openalex.org/W3017143921"],"related_works":["https://openalex.org/W7097837784","https://openalex.org/W7098824345","https://openalex.org/W2018434937","https://openalex.org/W7098343142","https://openalex.org/W2491147925","https://openalex.org/W2135248331","https://openalex.org/W4247278995","https://openalex.org/W1492469144","https://openalex.org/W2589196775","https://openalex.org/W2186990675"],"abstract_inverted_index":{"Classification":[0],"algorithms":[1,60],"are":[2],"difficult":[3],"to":[4,6,49,54],"apply":[5],"sequential":[7],"examples":[8],"because":[9],"there":[10],"is":[11,39],"a":[12,52],"vast":[13],"number":[14],"of":[15,33,36],"potentially":[16],"useful":[17],"features":[18,56,75],"for":[19,41,57],"describing":[20],"each":[21],"example.Past":[22],"work":[23],"on":[24,29,68],"feature":[25,43],"selection":[26],"has":[27],"focused":[28],"searching":[30],"the":[31,74],"space":[32],"all":[34],"subsets":[35],"features,":[37],"which":[38],"intractable":[40],"large":[42],"sets.We":[44],"adapt":[45],"sequence":[46],"mining":[47],"techniques":[48],"aEi":[50],"as":[51,62],"preprocessor":[53],"select":[55],"standard":[58],"classification":[59,81],"such":[61],"Naive":[63],"Bayes":[64],"and":[65],"Winnow.Our":[66],"experiments":[67],"three":[69],"different":[70],"datasets":[71],"show":[72],"that":[73],"produced":[76],"by":[77,83],"our":[78],"algorithm":[79],"improve":[80],"accuracy":[82],"lo-50%,":[84]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":8},{"year":2014,"cited_by_count":10},{"year":2013,"cited_by_count":10},{"year":2012,"cited_by_count":5}],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2016-06-24T00:00:00"}
