{"id":"https://openalex.org/W2554575047","doi":null,"title":"Enhancing pattern recognition using evolutionary computation for feature selection and extraction with application to the biochemistry of protein-water binding","display_name":"Enhancing pattern recognition using evolutionary computation for feature selection and extraction with application to the biochemistry of protein-water binding","publication_year":2000,"publication_date":"2000-01-01","ids":{"openalex":"https://openalex.org/W2554575047","mag":"2554575047"},"language":"en","primary_location":{"id":"mag:2554575047","doi":null,"is_oa":false,"landing_page_url":"http://dl.acm.org/citation.cfm?id=933576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5089550786","display_name":"William F. Punch","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"William F. Punch","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5021570308","display_name":"Michael L. Raymer","orcid":"https://orcid.org/0000-0003-2649-0792","observed_orcids":["https://orcid.org/0000-0003-2649-0792"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael L. Raymer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.8134999871253967,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.8134999871253967,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.02710000053048134,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.02449999935925007,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.9350000023841858},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":1.0},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"evolutionary computation","score":0.9520000219345093},{"id":"https://openalex.org/keywords/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.7549999952316284},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.9800000190734863},{"id":"https://openalex.org/keywords/hybrid-classifier","display_name":"hybrid classifier","score":0.9919999837875366},{"id":"https://openalex.org/keywords/exploratory-data-analysis","display_name":"exploratory data analysis","score":0.8320000171661377},{"id":"https://openalex.org/keywords/data-mining","display_name":"data mining","score":0.5490000247955322},{"id":"https://openalex.org/keywords/protein-chemistry","display_name":"protein chemistry","score":0.41499999165534973}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7562048435211182},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7096725106239319},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6793453097343445},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6616474986076355},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.64487224817276},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6419720649719238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6135502457618713},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.5420523285865784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4588947296142578},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.41550880670547485},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3651210367679596},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3218722343444824}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2554575047","doi":null,"is_oa":false,"landing_page_url":"http://dl.acm.org/citation.cfm?id=933576","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2135190479","https://openalex.org/W294660780","https://openalex.org/W2149624569","https://openalex.org/W1961528379","https://openalex.org/W7096992513","https://openalex.org/W7099498680","https://openalex.org/W2135304732","https://openalex.org/W2032254677","https://openalex.org/W2909459367","https://openalex.org/W2277659813"],"abstract_inverted_index":{"Statistical":[0],"pattern":[1,33],"recognition":[2],"techniques":[3,69,91],"classify":[4],"objects":[5],"in":[6,48,127,147,165,189,210,262],"terms":[7,166],"of":[8,12,18,31,40,54,73,98,113,117,167,171,232,264,285,296,299],"a":[9,25,110,122,150,283],"representative":[10],"set":[11,231,284],"features.":[13,80,120],"The":[14,247,269],"selection":[15,162,195,250],"and":[16,60,86,133,153,163,196,199,214,243,251,259,281,287],"quality":[17],"the":[19,29,38,45,52,74,96,118,169,183,192,200,221,226,230,294],"features":[20,43,47,76,99,172,289],"representing":[21],"each":[22,105],"object":[23],"have":[24],"considerable":[26],"bearing":[27],"on":[28,182],"success":[30],"subsequent":[32],"classification.":[34],"Feature":[35],"extraction":[36,68,164,197,252],"is":[37,125,145,225,254,272],"process":[39],"deriving":[41],"new":[42,79,106,123,179],"from":[44],"original":[46,75,119],"order":[49],"to":[50,77,155,206,220,228,239,256,274],"reduce":[51,95],"cost":[53],"feature":[55,67,107,129,131,161,194,211,234,249],"measurement,":[56],"increase":[57],"classifier":[58,134,186,271],"efficiency,":[59,89],"allow":[61],"higher":[62],"classification":[63,88,176,215],"accuracy.":[64,177,216],"Many":[65],"current":[66,158],"involve":[70],"linear":[71,111,233],"transformations":[72],"produce":[78],"While":[81],"useful":[82],"for":[83,160],"data":[84,241,245],"visualization":[85],"increasing":[87],"these":[90],"do":[92],"not":[93],"necessarily":[94],"number":[97,170],"that":[100,290],"must":[101],"be":[102,109],"measured":[103],"since":[104],"may":[108],"combination":[112],"some":[114],"or":[115],"all":[116],"Here":[121],"approach":[124],"presented":[126,223],"which":[128],"selection,":[130],"extraction,":[132],"training":[135],"are":[136,187,204],"performed":[137],"simultaneously":[138],"using":[139],"evolutionary":[140],"computing":[141],"(EC).":[142],"This":[143],"method":[144],"tested":[146],"conjunction":[148,190],"with":[149,191,278,293],"k-nearest-neighbors":[151],"classifier,":[152],"shown":[154,205],"outperform":[156],"other":[157,297],"methods":[159,222],"minimizing":[168],"employed":[173],"while":[174],"maximizing":[175],"Two":[178],"classifiers":[180,203],"based":[181],"naive":[184],"Bayes":[185],"developed":[188],"EC":[193,238,248],"technique,":[198],"resulting":[201,270],"hybrid":[202],"yield":[207],"further":[208],"improvements":[209],"subset":[212],"parsimony":[213],"A":[217],"key":[218],"advantage":[219],"here":[224],"ability":[227],"examine":[229],"weights":[235],"produced":[236],"by":[237],"perform":[240],"mining":[242],"exploratory":[244],"analysis.":[246],"technique":[253],"applied":[255],"an":[257],"important":[258],"difficult":[260],"problem":[261],"biochemistry\u2014classification":[263],"potential":[265],"protein-water":[266,300],"binding":[267],"sites.":[268],"able":[273],"identify":[275],"water-binding":[276],"sites":[277],"\u223c68%":[279],"accuracy,":[280],"identifies":[282],"physical":[286],"chemical":[288],"correspond":[291],"well":[292],"results":[295],"studies":[298],"binding.":[301]},"counts_by_year":[],"updated_date":"2026-10-01T08:54:57.152042","created_date":"2016-11-30T00:00:00"}