Confidence in classification : A bayesian approach
Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic choice under certain circumstances when there is no single "true" c...
Gespeichert in:
| Veröffentlicht in: | Journal of classification 2006-09, Vol.23 (2), p.199-220 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 220 |
|---|---|
| container_issue | 2 |
| container_start_page | 199 |
| container_title | Journal of classification |
| container_volume | 23 |
| creator | KRZANOWSKI, Wojtek J FIELDSEND, Jonathan E BAILEY, Trevor C EVERSON, Richard M PARTRIDGE, Derek SCHETININ, Vitaly |
| description | Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic choice under certain circumstances when there is no single "true" classifier for a given data set. Modern computing capabilities can easily support the Markov chain Monte Carlo sampling that is necessary to carry out the calculations involved, but the information available in these samples is not at present being fully utilised. We show how it can be allied to known results concerning the "reject option" in order to produce an assessment of the confidence that can be ascribed to particular classifications, and how these confidence measures can be used to compare the performances of classifiers. Incorporating these confidence measures can alter the apparent ranking of classifiers as given by straightforward success or error rates. Several possible methods for obtaining confidence assessments are described, and compared on a range of data sets using the Bayesian probabilistic nearest-neighbour classifier.[PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1007/s00357-006-0013-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_57646799</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2094124911</sourcerecordid><originalsourceid>FETCH-LOGICAL-c376t-cbc9442446d531b5ef5d74ffe010a66ec0d16efcc5a42d69cc7a586eb42bf7f3</originalsourceid><addsrcrecordid>eNpdkE1LAzEQhoMoWKs_wNsi6G012clH11spfkHBS-9hdjbBlG22Ju2h_96UCoKHYS7POx8PY7eCPwrOzVPmHJSpOdelBNRwxiZCQlMLkHDOJlwYXctGzy7ZVc5rXjJamwlrFmP0oXeRXBViRQPmHHwg3IUxVs_VvOrw4HLAWOF2m0akr2t24XHI7ua3T9nq9WW1eK-Xn28fi_myJjB6V1NHrZSNlLpXIDrlvOqN9N5xwVFrR7wX2nkihbLpdUtkUM2062TTeeNhyh5OY8vW773LO7sJmdwwYHTjPltltNSmbQt49w9cj_sUy2nWAHClClkgcYIojTkn5-02hQ2mgxXcHg3ak0FbDNqjQQslc_87GDPh4BNGCvkvOANR3gP4AUGHb3E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>733055576</pqid></control><display><type>article</type><title>Confidence in classification : A bayesian approach</title><source>SpringerNature Journals</source><creator>KRZANOWSKI, Wojtek J ; FIELDSEND, Jonathan E ; BAILEY, Trevor C ; EVERSON, Richard M ; PARTRIDGE, Derek ; SCHETININ, Vitaly</creator><creatorcontrib>KRZANOWSKI, Wojtek J ; FIELDSEND, Jonathan E ; BAILEY, Trevor C ; EVERSON, Richard M ; PARTRIDGE, Derek ; SCHETININ, Vitaly</creatorcontrib><description>Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic choice under certain circumstances when there is no single "true" classifier for a given data set. Modern computing capabilities can easily support the Markov chain Monte Carlo sampling that is necessary to carry out the calculations involved, but the information available in these samples is not at present being fully utilised. We show how it can be allied to known results concerning the "reject option" in order to produce an assessment of the confidence that can be ascribed to particular classifications, and how these confidence measures can be used to compare the performances of classifiers. Incorporating these confidence measures can alter the apparent ranking of classifiers as given by straightforward success or error rates. Several possible methods for obtaining confidence assessments are described, and compared on a range of data sets using the Bayesian probabilistic nearest-neighbour classifier.[PUBLICATION ABSTRACT]</description><identifier>ISSN: 0176-4268</identifier><identifier>EISSN: 1432-1343</identifier><identifier>DOI: 10.1007/s00357-006-0013-3</identifier><language>eng</language><publisher>New York, NY: Springer</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Automatic classification ; Bayesian analysis ; Bayesian techniques ; Computer applications ; Computer science; control theory; systems ; Confidence ; Decision theory ; Exact sciences and technology ; Expert systems ; Knowledge representation ; Markov analysis ; Mathematics ; Multivariate analysis ; Probability and statistics ; Sciences and techniques of general use ; Statistics ; Studies ; Theoretical computing</subject><ispartof>Journal of classification, 2006-09, Vol.23 (2), p.199-220</ispartof><rights>2007 INIST-CNRS</rights><rights>Springer Science + Business Media Inc. 2006</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-cbc9442446d531b5ef5d74ffe010a66ec0d16efcc5a42d69cc7a586eb42bf7f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27928,27929</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18312443$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>KRZANOWSKI, Wojtek J</creatorcontrib><creatorcontrib>FIELDSEND, Jonathan E</creatorcontrib><creatorcontrib>BAILEY, Trevor C</creatorcontrib><creatorcontrib>EVERSON, Richard M</creatorcontrib><creatorcontrib>PARTRIDGE, Derek</creatorcontrib><creatorcontrib>SCHETININ, Vitaly</creatorcontrib><title>Confidence in classification : A bayesian approach</title><title>Journal of classification</title><description>Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic choice under certain circumstances when there is no single "true" classifier for a given data set. Modern computing capabilities can easily support the Markov chain Monte Carlo sampling that is necessary to carry out the calculations involved, but the information available in these samples is not at present being fully utilised. We show how it can be allied to known results concerning the "reject option" in order to produce an assessment of the confidence that can be ascribed to particular classifications, and how these confidence measures can be used to compare the performances of classifiers. Incorporating these confidence measures can alter the apparent ranking of classifiers as given by straightforward success or error rates. Several possible methods for obtaining confidence assessments are described, and compared on a range of data sets using the Bayesian probabilistic nearest-neighbour classifier.[PUBLICATION ABSTRACT]</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Automatic classification</subject><subject>Bayesian analysis</subject><subject>Bayesian techniques</subject><subject>Computer applications</subject><subject>Computer science; control theory; systems</subject><subject>Confidence</subject><subject>Decision theory</subject><subject>Exact sciences and technology</subject><subject>Expert systems</subject><subject>Knowledge representation</subject><subject>Markov analysis</subject><subject>Mathematics</subject><subject>Multivariate analysis</subject><subject>Probability and statistics</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>Studies</subject><subject>Theoretical computing</subject><issn>0176-4268</issn><issn>1432-1343</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkE1LAzEQhoMoWKs_wNsi6G012clH11spfkHBS-9hdjbBlG22Ju2h_96UCoKHYS7POx8PY7eCPwrOzVPmHJSpOdelBNRwxiZCQlMLkHDOJlwYXctGzy7ZVc5rXjJamwlrFmP0oXeRXBViRQPmHHwg3IUxVs_VvOrw4HLAWOF2m0akr2t24XHI7ua3T9nq9WW1eK-Xn28fi_myJjB6V1NHrZSNlLpXIDrlvOqN9N5xwVFrR7wX2nkihbLpdUtkUM2062TTeeNhyh5OY8vW773LO7sJmdwwYHTjPltltNSmbQt49w9cj_sUy2nWAHClClkgcYIojTkn5-02hQ2mgxXcHg3ak0FbDNqjQQslc_87GDPh4BNGCvkvOANR3gP4AUGHb3E</recordid><startdate>20060901</startdate><enddate>20060901</enddate><creator>KRZANOWSKI, Wojtek J</creator><creator>FIELDSEND, Jonathan E</creator><creator>BAILEY, Trevor C</creator><creator>EVERSON, Richard M</creator><creator>PARTRIDGE, Derek</creator><creator>SCHETININ, Vitaly</creator><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M0N</scope><scope>M1O</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20060901</creationdate><title>Confidence in classification : A bayesian approach</title><author>KRZANOWSKI, Wojtek J ; FIELDSEND, Jonathan E ; BAILEY, Trevor C ; EVERSON, Richard M ; PARTRIDGE, Derek ; SCHETININ, Vitaly</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-cbc9442446d531b5ef5d74ffe010a66ec0d16efcc5a42d69cc7a586eb42bf7f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Automatic classification</topic><topic>Bayesian analysis</topic><topic>Bayesian techniques</topic><topic>Computer applications</topic><topic>Computer science; control theory; systems</topic><topic>Confidence</topic><topic>Decision theory</topic><topic>Exact sciences and technology</topic><topic>Expert systems</topic><topic>Knowledge representation</topic><topic>Markov analysis</topic><topic>Mathematics</topic><topic>Multivariate analysis</topic><topic>Probability and statistics</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><topic>Studies</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>KRZANOWSKI, Wojtek J</creatorcontrib><creatorcontrib>FIELDSEND, Jonathan E</creatorcontrib><creatorcontrib>BAILEY, Trevor C</creatorcontrib><creatorcontrib>EVERSON, Richard M</creatorcontrib><creatorcontrib>PARTRIDGE, Derek</creatorcontrib><creatorcontrib>SCHETININ, Vitaly</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Computing Database</collection><collection>Library Science Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of classification</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>KRZANOWSKI, Wojtek J</au><au>FIELDSEND, Jonathan E</au><au>BAILEY, Trevor C</au><au>EVERSON, Richard M</au><au>PARTRIDGE, Derek</au><au>SCHETININ, Vitaly</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Confidence in classification : A bayesian approach</atitle><jtitle>Journal of classification</jtitle><date>2006-09-01</date><risdate>2006</risdate><volume>23</volume><issue>2</issue><spage>199</spage><epage>220</epage><pages>199-220</pages><issn>0176-4268</issn><eissn>1432-1343</eissn><abstract>Bayesian classification is currently of considerable interest. It provides a strategy for eliminating the uncertainty associated with a particular choice of classifiermodel parameters, and is the optimal decision-theoretic choice under certain circumstances when there is no single "true" classifier for a given data set. Modern computing capabilities can easily support the Markov chain Monte Carlo sampling that is necessary to carry out the calculations involved, but the information available in these samples is not at present being fully utilised. We show how it can be allied to known results concerning the "reject option" in order to produce an assessment of the confidence that can be ascribed to particular classifications, and how these confidence measures can be used to compare the performances of classifiers. Incorporating these confidence measures can alter the apparent ranking of classifiers as given by straightforward success or error rates. Several possible methods for obtaining confidence assessments are described, and compared on a range of data sets using the Bayesian probabilistic nearest-neighbour classifier.[PUBLICATION ABSTRACT]</abstract><cop>New York, NY</cop><pub>Springer</pub><doi>10.1007/s00357-006-0013-3</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0176-4268 |
| ispartof | Journal of classification, 2006-09, Vol.23 (2), p.199-220 |
| issn | 0176-4268 1432-1343 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_57646799 |
| source | SpringerNature Journals |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Automatic classification Bayesian analysis Bayesian techniques Computer applications Computer science control theory systems Confidence Decision theory Exact sciences and technology Expert systems Knowledge representation Markov analysis Mathematics Multivariate analysis Probability and statistics Sciences and techniques of general use Statistics Studies Theoretical computing |
| title | Confidence in classification : A bayesian approach |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T13%3A55%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Confidence%20in%20classification%20:%20A%20bayesian%20approach&rft.jtitle=Journal%20of%20classification&rft.au=KRZANOWSKI,%20Wojtek%20J&rft.date=2006-09-01&rft.volume=23&rft.issue=2&rft.spage=199&rft.epage=220&rft.pages=199-220&rft.issn=0176-4268&rft.eissn=1432-1343&rft_id=info:doi/10.1007/s00357-006-0013-3&rft_dat=%3Cproquest_cross%3E2094124911%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=733055576&rft_id=info:pmid/&rfr_iscdi=true |