Methods for Designing Multiple Classifier Systems
In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buchkapitel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 87 |
|---|---|
| container_issue | |
| container_start_page | 78 |
| container_title | |
| container_volume | 2096 |
| creator | Roli, Fabio Giacinto, Giorgio Vernazza, Gianni |
| description | In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open. |
| doi_str_mv | 10.1007/3-540-48219-9_8 |
| format | Book Chapter |
| fullrecord | <record><control><sourceid>proquest_pasca</sourceid><recordid>TN_cdi_pascalfrancis_primary_1015720</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EBC6284099_73_90</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2268-a0af7ec8402972ed2601f09c76fc2b7c9d264e57023e7e1d193700e6409361cd3</originalsourceid><addsrcrecordid>eNotkDtTwzAQhMVzEkJqWhe0gtPDlq5kwnOGDAVQaxTlHAzGNpIp8u8RgWtubndvi4-xMwEXAsBcKl5q4NpKgRyd3WMnKgu7G_fZVFRCcKU0HrA5GrvzpLS6OmRTUCA5Gq2O2URbtLpEO2HzlN4hjxIIFqdMLGl869epqPtYXFNqNl3TbYrldzs2Q0vFovUpNXVDsXjeppE-0yk7qn2baP6_Z-z19uZlcc8fn-4eFlePPEhZWe7B14aC1SDRSFrLCkQNGExVB7kyAbOiqTQgFRkSa4HKAFClAVUlwlrN2Plf7-BT8G0dfRea5IbYfPq4dQJEaSTkGP-Lpex0G4pu1fcfKfvul6BTLkNxO2IuE8x5-V8b-69vSqOj34dA3Rh9G978MFJMrsoUAdEZ5RDUDwrobCw</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>book_chapter</recordtype><pqid>EBC6284099_73_90</pqid></control><display><type>book_chapter</type><title>Methods for Designing Multiple Classifier Systems</title><source>Springer Books</source><creator>Roli, Fabio ; Giacinto, Giorgio ; Vernazza, Gianni</creator><contributor>Kittler, Josef ; Roli, Fabio ; Kittler, Josef ; Roli, Fabio</contributor><creatorcontrib>Roli, Fabio ; Giacinto, Giorgio ; Vernazza, Gianni ; Kittler, Josef ; Roli, Fabio ; Kittler, Josef ; Roli, Fabio</creatorcontrib><description>In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540422846</identifier><identifier>ISBN: 3540422846</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540482199</identifier><identifier>EISBN: 9783540482192</identifier><identifier>DOI: 10.1007/3-540-48219-9_8</identifier><identifier>OCLC: 48984598</identifier><identifier>LCCallNum: Q334-342</identifier><language>eng</language><publisher>Germany: Springer Berlin / Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Classifier Ensemble ; Combination Function ; Computer science; control theory; systems ; Exact sciences and technology ; Generalisation Diversity ; Heuristic Rule ; Image processing ; Learning and adaptive systems ; Pattern recognition ; Radial Basis Function</subject><ispartof>Lecture notes in computer science, 2001, Vol.2096, p.78-87</ispartof><rights>Springer-Verlag Berlin Heidelberg 2001</rights><rights>2001 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2268-a0af7ec8402972ed2601f09c76fc2b7c9d264e57023e7e1d193700e6409361cd3</citedby><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://ebookcentral.proquest.com/covers/6284099-l.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-48219-9_8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-48219-9_8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4036,4037,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1015720$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Kittler, Josef</contributor><contributor>Roli, Fabio</contributor><contributor>Kittler, Josef</contributor><contributor>Roli, Fabio</contributor><creatorcontrib>Roli, Fabio</creatorcontrib><creatorcontrib>Giacinto, Giorgio</creatorcontrib><creatorcontrib>Vernazza, Gianni</creatorcontrib><title>Methods for Designing Multiple Classifier Systems</title><title>Lecture notes in computer science</title><description>In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Classifier Ensemble</subject><subject>Combination Function</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Generalisation Diversity</subject><subject>Heuristic Rule</subject><subject>Image processing</subject><subject>Learning and adaptive systems</subject><subject>Pattern recognition</subject><subject>Radial Basis Function</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540422846</isbn><isbn>3540422846</isbn><isbn>3540482199</isbn><isbn>9783540482192</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2001</creationdate><recordtype>book_chapter</recordtype><recordid>eNotkDtTwzAQhMVzEkJqWhe0gtPDlq5kwnOGDAVQaxTlHAzGNpIp8u8RgWtubndvi4-xMwEXAsBcKl5q4NpKgRyd3WMnKgu7G_fZVFRCcKU0HrA5GrvzpLS6OmRTUCA5Gq2O2URbtLpEO2HzlN4hjxIIFqdMLGl869epqPtYXFNqNl3TbYrldzs2Q0vFovUpNXVDsXjeppE-0yk7qn2baP6_Z-z19uZlcc8fn-4eFlePPEhZWe7B14aC1SDRSFrLCkQNGExVB7kyAbOiqTQgFRkSa4HKAFClAVUlwlrN2Plf7-BT8G0dfRea5IbYfPq4dQJEaSTkGP-Lpex0G4pu1fcfKfvul6BTLkNxO2IuE8x5-V8b-69vSqOj34dA3Rh9G978MFJMrsoUAdEZ5RDUDwrobCw</recordid><startdate>2001</startdate><enddate>2001</enddate><creator>Roli, Fabio</creator><creator>Giacinto, Giorgio</creator><creator>Vernazza, Gianni</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2001</creationdate><title>Methods for Designing Multiple Classifier Systems</title><author>Roli, Fabio ; Giacinto, Giorgio ; Vernazza, Gianni</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2268-a0af7ec8402972ed2601f09c76fc2b7c9d264e57023e7e1d193700e6409361cd3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Classifier Ensemble</topic><topic>Combination Function</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Generalisation Diversity</topic><topic>Heuristic Rule</topic><topic>Image processing</topic><topic>Learning and adaptive systems</topic><topic>Pattern recognition</topic><topic>Radial Basis Function</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Roli, Fabio</creatorcontrib><creatorcontrib>Giacinto, Giorgio</creatorcontrib><creatorcontrib>Vernazza, Gianni</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Roli, Fabio</au><au>Giacinto, Giorgio</au><au>Vernazza, Gianni</au><au>Kittler, Josef</au><au>Roli, Fabio</au><au>Kittler, Josef</au><au>Roli, Fabio</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Methods for Designing Multiple Classifier Systems</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2001</date><risdate>2001</risdate><volume>2096</volume><spage>78</spage><epage>87</epage><pages>78-87</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540422846</isbn><isbn>3540422846</isbn><eisbn>3540482199</eisbn><eisbn>9783540482192</eisbn><abstract>In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose“ paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.</abstract><cop>Germany</cop><pub>Springer Berlin / Heidelberg</pub><doi>10.1007/3-540-48219-9_8</doi><oclcid>48984598</oclcid><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2001, Vol.2096, p.78-87 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_1015720 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Classifier Ensemble Combination Function Computer science control theory systems Exact sciences and technology Generalisation Diversity Heuristic Rule Image processing Learning and adaptive systems Pattern recognition Radial Basis Function |
| title | Methods for Designing Multiple Classifier Systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-04T19%3A31%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pasca&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=bookitem&rft.atitle=Methods%20for%20Designing%20Multiple%20Classifier%20Systems&rft.btitle=Lecture%20notes%20in%20computer%20science&rft.au=Roli,%20Fabio&rft.date=2001&rft.volume=2096&rft.spage=78&rft.epage=87&rft.pages=78-87&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540422846&rft.isbn_list=3540422846&rft_id=info:doi/10.1007/3-540-48219-9_8&rft_dat=%3Cproquest_pasca%3EEBC6284099_73_90%3C/proquest_pasca%3E%3Curl%3E%3C/url%3E&rft.eisbn=3540482199&rft.eisbn_list=9783540482192&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=EBC6284099_73_90&rft_id=info:pmid/&rfr_iscdi=true |